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Machine learning and role of artificial intelligence in optimizing work performance and employee behavior

Materials Today: Proceedings 2022年 51卷 2327-2331页

作者： K.K. Ramachandran A. Apsara Saleth Mary Shibani Hawladar D. Asokk Bandi Bhaskar J.R. Pitroda Professor Management Dr G R D College of Science India Faculty Centre for Tourism and Hotel Management Madurai Kamaraj University Tamil Nadu India Research Scholar BS Abdur Rahman Crescent Institute of Science and Technology Chennai India Faculty BS Abdur Rahman Crescent Institute of Science and Technology Chennai India School of Computer Science and Artificial Intelligence SR University Telangana India Civil Engineering Department BVM Engineering College Gujarat Technological University India

In today’s business world, firms are using analytics to diving deeper into their data to improve productivity, acquire a competitive edge, and boost their bottom lines. That is why businesses are eager to integrate machine learning (ML) and artificial intelligence (AI) because they want a faster, more accurate result. In addition, machine learning and artificial intelligence improved productivity by reducing repetitive tasks. As a result, nearly every sector in the world intends to use artificial intelligence and ML. artificial intelligence and ML have various roles in business, ranging from enhancing employee and customer interactions to discovering patterns in massive amounts of data to automating monotonous activities. The goal is to seize the opportunity that artificial intelligence provides in employee success and productivity. Implementing artificial intelligence and machine learning may be a vital tool for any company seeking quantitative help in their decision-making since it can analyze vast quantities of data and spit forth trend directions and actionable suggestions. AI helps employees and businesses to set and achieve better goals results. This paper highlights the impact of using artificial intelligence and ML to enhance employee behaviour and work outcome. This research also provides insights into artificial intelligence, which will usher in a new era in industry.

关键词： artificial intelligence Machine learning Productivity Employee behaviour Automation

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Time-aware bike flow prediction framework with dynamic edge fusion and memory integration

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Knowledge-Based Systems 2025年 322卷

作者： M.Rajeh, Taha Li, Tianrui Luo, Zhpeng Javed, Muhammad Hafeez Alhaek, Fares School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu611756 China Stirling College Chengdu University Chengdu610106 China Computing Science Faculty of Natural Sciences University of Stirling StirlingFK9 4LA United Kingdom

Bike-Sharing Systems (BSSs) have become a popular solution to urban sustainability concerns, providing affordable and environmentally friendly transportation. However, modeling and predicting shared-bike flow patterns are essential for developing intelligent transportation systems and facilitating city management. Despite the existence of several inflow and outflow prediction models, certain unresolved issues remain, including the inability to differentiate and capture heterogeneous dynamic relationships within daily flows, and the suboptimal utilization of historical flow data for forecasting. To address these limitations, we propose MVRC (Multi-View Relational Framework based on Clustering), a novel deep-learning framework for precise inflow and outflow prediction. The MVRC framework models three types of heterogeneous dynamic flow relationships during different periods of the day. Initially, we introduce a novel clustering method to group nearby stations exhibiting similar patterns, followed by creating a dynamic memory module to store and update edge attributes based on the time of day. Furthermore, the dynamic edge fusion mechanism effectively integrates heterogeneous, time-evolving flow features, capturing their varying impacts. To further enhance the model's capabilities, an offline component that leverages historical data is incorporated, improving performance during testing. Extensive experiments conducted on real datasets from New York City and Washington, D.C., demonstrate the superior prediction efficiency and framework effectiveness of the MVRC framework compared to existing state-of-the-art techniques. These results highlight the practicality and efficacy of MVRC in terms of prediction accuracy and stability. © 2025

关键词： Urban transportation

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Penetration trajectory optimization for the hypersonic gliding vehicle encountering two interceptors

arXiv

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arXiv 2022年

作者： Shen, Zhipeng Yu, Jianglong Dong, Xiwang Hua, Yongzhao Ren, Zhang School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beihang University Beijing100191 China Institute of Artificial Intelligence Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

The penetration trajectory optimization problem for the hypersonic gliding vehicle (HGV) encountering two interceptors is investigated. The HGV penetration trajectory optimization problem considering the terminal target area is formulated as a nonconvex optimal control problem. The nonconvex optimal control problem is transformed into a second-order cone programming (SOCP) problem, which can be solved by state-of-the-art interior-point methods. In addition, a penetration strategy that only requires the initial line-of-sight angle information of the interceptors is proposed. The convergent trajectory obtained by the proposed method allows the HGV to evade two interceptors and reach the target area successfully. Furthermore, a successive SOCP method with a variable trust region is presented, which is critical to balancing the trade-off between time consumption and optimality. Finally, the effectiveness and performance of the proposed method are verified by numerical simulations. Copyright © 2022, The Authors. All rights reserved.

关键词： Numerical methods

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Deep learning-driven multi-user wavelet NOMA for user centric cell-free massive MIMO communications

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Alexandria engineering Journal 2025年 128卷 533-543页

作者： Rabia Arshad Sobia Baig Saad Aslam Muneeb Ahmad Shahid Mumtaz Department of Computer Science University of Central Punjab Avenue 1 Khayaban-e-Jinnah Road Johar Town Lahore 54000 Punjab Pakistan Department of Electrical and Computer Engineering Energy Research Center COMSATS University 1.5 KM Defence Road off Raiwand Road LDA Avenue Phase 1 Lahore 54000 Punjab Pakistan School of Computing and Artificial Intelligence Faculty of Engineering and Technology Sunway University Petaling Jaya Selangor 47500 Malaysia Department of IT Convergence Engineering Kumoh National Institute of Technology Gumi 39177 South Korea Department of Engineering Nottingham Trent University 50 Shakespeare St Nottingham NG1 4BU United Kingdom

The evolution of Cell-Free Massive MIMO (CF-mMIMO) systems in the sixth-generation (6G) communications brings notable benefits including enhanced capacity, broader coverage, and greater reliability. However, these advanced systems may be subjected to critical challenges like, exponential growth in the user connectivity, precise channel estimation, and effective mitigation of the inter-user interference. This article addresses these challenges through Deep Learning (DL) for accurate channel estimation and a robust Wavelet Transform based Non-Orthogonal Multiple Access (NOMA) scheme to mitigate the inter-user interference in a user-centric CF-mMIMO system. By eliminating the reliance on pilot-assisted channel estimation, the DL-based approach achieves higher accuracy and lowers transmission overhead in a multi-user scenario. The results highlight the superiority of DL-based channel estimation for a CF-mMIMO system employing wavelet NOMA scheme over traditional methods, showing a 17% reduction in bit error rate (BER) and a 15% improvement in achievable sum-rate.

关键词： User Centric(UC) Cell-Free Massive MIMO Deep Learning(DL) Channel Estimation (CE) Non-Orthogonal Multiple Access(NOMA) Wavelet transform

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Predicting the Impacts of the Environment on the Probability and Severity of Electrical Injuries by AIML

Predicting the Impacts of the Environment on the Probability...

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New Frontiers in Communication, Automation, Management and Security (ICCAMS), International Conference on

作者： Araddhana Arvind Deshmukh K Parish Venkata Kumar B.Anjanee Kumar Pramoda Patro Pavitar Parkash Singh N. Ashokkumar Department of Artificial Intelligence and Data science Marathwada Mitra Mandal College of Engineering Pune India Department of Computer Applications Velagapudi Ramakrishna Siddhartha Engineering College (Autonomous) Vijayawada India Electronics and Communication Engineering Dadi institute of engineering and technology Visakhapatnam India Department of Mathematics Koneru Lakshmaiah Education Foundation Hyderabad India Department of Management Lovely Professional University Phagwara India Department of ECE Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupati India

ISBN: (数字)9798350317060

ISBN: (纸本)9798350317077

The acceleration of global warming and environmental degradation has been attributed to industrial growth and fossil fuel utilization. Consequently, this phenomenon has resulted in a rise in the frequency and intensity of natural disasters. Due to their predominantly outdoor installation, generators are subject to significant impacts. The occurrence of natural disasters has led to an increase in accidents associated with electrical equipment. This paper analyses statistical data on electrical fires, electric shock accidents, and electricity-related accidents caused by climate change in domestic settings to determine the associated risk *** this study, we propose a risk assessment model for electrical fires, explicitly focusing on every month. Our approach involves analysing data on electrical hazards in different regional areas, particularly metropolitan cities, considering climatic conditions such as temperature and humidity. Additionally, we aim to evaluate the accident risk rating associated with natural disasters, specifically those related to low and high-voltage equipment. In this study, a risk analysis model has been developed to assess electrical hazards in different regions and equipment types. The model presented herein serves as a foundational predictive tool. Hence, offering electrical security amenities in the forthcoming period is feasible by giving a risk estimation map delineating potential dangers specific to each geographical area and wiring category via web platforms or mobile applications utilizing the investigation data provided. Additionally, it is imperative to enhance the resilience and dependability of electrical components as a proactive measure against electrocute ions resulting from climate change-induced emergencies.

关键词： Wiring Temperature distribution Electric potential Analytical models Disasters Urban areas Humidity

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Enhancing Natural Language Processing in Multilingual Chatbots for Cross-Cultural Communication

Enhancing Natural Language Processing in Multilingual Chatbo...

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Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), International Conference on

作者： Myagmarsuren Orosoo Indrajit Goswami Fredrick Ruban Alphonse Gulnaz Fatma Manikandan Rengarajan B Kiran Bala Mongolian National University of Education General Management Strategy and HRM Alliance School of Business Alliance University Bengaluru Department of Languages Institute of Finance and International Management Bengaluru Dept. of English Jazan University Jazan Saudi Arabia Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science K.Ramakrishnan college of Engineering Trichy

In order to promote smooth cross-cultural communication, this research focuses on improving Natural Language Processing (NLP) features within multilingual Chabots. The need for efficient communication across various linguistic and cultural barriers has grown as the world becomes more interconnected. In this context, multilingual Chabot's are essential tools that facilitate easy communication between users of various cultural backgrounds. In order to better understand and respond to a wide range of linguistic details, idioms, and cultural allusions, the research explores ways to improve NLP algorithms. This research investigates how machine learning techniques can be integrated to improve the Chabot's ability to adapt to various linguistic patterns, enabling it to acquire new language skills over time. By implementing an extensive and flexible approach, the proposed approach seeks to transform Natural Language Processing in Multilingual Chatbots for Cross-Cultural Communication. In order to identify language-specific patterns and specifications, the methodology starts with the collection of a diverse dataset that is representative of linguistic variations and cultural contexts. It then uses advanced linguistic analysis techniques like phonetic analysis. The next step is cultural context modelling, which entails building a database enhanced with cultural allusions and language variations unique to a given context that are obtained from various sources. Transfer learning, reinforcement learning for dialogue, and modern algorithms such as Multilingual Unsupervised and Supervised Embeddings (MUSE) for cross-lingual embeddings are all included in machine learning integration. In order to obtain information about language precision and cultural sensitivity, the methodology incorporates interactive surveys and sentiment analysis into a strong user feedback mechanism. The proposed model achieved 98% performance.

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Vietnamese event detection based on Chinese information and Vietnamese syntax guidance 20

Vietnamese event detection based on Chinese information and ...

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20th Chinese National Conference on Computational Linguistics, CCL 2021

作者： Chen, Long Guo, Junjun Zhang, Yafei Gao, Shengxiang Yu, Zhengtao Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Kunming650500 China

Current event detection models based on deep learning rely on a sufficient amount of labeled data and only focus on specific in-formation such as trigger words. However, the scarcity of annotation data for Vietnamese news events and the ambiguity of event types have brought great challenges to Vietnamese event detection. According to the fea-ture that sentences expressing the same viewpoint but different languages usually have the same or similar semantic components, this paper proposes a Vietnamese event detection framework that combines Chinese information and Vietnamese syntax. First, use the shared encoder strategy and cross-attention network to integrate Chinese semantic information into Vietnamese, then use graph convolutional network to obtain Vietnamese representation based on Vietnamese dependency syntactic information. Finally, the Vietnamese semantic representation based on Chinese event type information is extracted through the event type perception network to realize Vietnamese news event detection. Experimental results show that under the guidance of Chinese information and Vietnamese syntax, Vietnamese event detection has achieved good results. © 2021 China National Conference on Computational Linguistics Published under Creative Commons Attribution 4.0 International License

关键词： Deep learning

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Semantically Meaningful Class Prototype Learning for One-Shot Image Semantic Segmentation

arXiv

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arXiv 2021年

作者： Chen, Tao Xie, Guo-Sen Yao, Yazhou Wang, Qiong Shen, Fumin Tang, Zhenmin Zhang, Jian The School of Computer Science and Engineering Nanjing University of Science and Technology China The Inception Institute of Artificial Intelligence Abu Dhabi United Arab Emirates The School of Computer Science and Engineering University of Electronic Science and Technology of China China The School of Electrical & Data Engineering University of Technology Sydney Australia

One-shot semantic image segmentation aims to segment the object regions for the novel class with only one annotated image. Recent works adopt the episodic training strategy to mimic the expected situation at testing time. However, these existing approaches simulate the test conditions too strictly during the training process, and thus cannot make full use of the given label information. Besides, these approaches mainly focus on the foreground-background target class segmentation setting. They only utilize binary mask labels for training. In this paper, we propose to leverage the multi-class label information during the episodic training. It will encourage the network to generate more semantically meaningful features for each category. After integrating the target class cues into the query features, we then propose a pyramid feature fusion module to mine the fused features for the final classifier. Furthermore, to take more advantage of the support image-mask pair, we propose a self-prototype guidance branch to support image segmentation. It can constrain the network for generating more compact features and a robust prototype for each semantic class. For inference, we propose a fused prototype guidance branch for the segmentation of the query image. Specifically, we leverage the prediction of the query image to extract the pseudo-prototype and combine it with the initial prototype. Then we utilize the fused prototype to guide the final segmentation of the query image. Extensive experiments demonstrate the superiority of our proposed approach. The source codes and models have been made available at https: //***/NUST-Machine-intelligence-Laboratory/SMCP. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Semantic data Management in data Lakes

arXiv

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arXiv 2023年

作者： Hoseini, Sayed Theissen-Lipp, Johannes Quix, Christoph Department of Electrical Engineering and Computer Science Hochschule Niederrhein University of Applied Sciences Reinarzstrasse 49 NRW Krefeld47805 Germany Databases and Information Systems RWTH Aachen University Ahornstraße 55 Aachen NRW Aachen52074 Germany Department of Data Science and Artificial Intelligence Fraunhofer Institute for Applied Information Technology FIT Schloss Birlinghoven NRW St. Augustin53757 Germany

In recent years, data lakes emerged as a way to manage large amounts of heterogeneous data for modern data analytics. One way to prevent data lakes from turning into inoperable data swamps is semantic data management. Some approaches propose the linkage of metadata to knowledge graphs based on the Linked data principles to provide more meaning and semantics to the data in the lake. Such a semantic layer may be utilized not only for data management but also to tackle the problem of data integration from heterogeneous sources, in order to make data access more expressive and interoperable. In this survey, we review recent approaches with a specific focus on the application within data lake systems and scalability to Big data. We classify the approaches into (i) basic semantic data management, (ii) semantic modeling approaches for enriching metadata in data lakes, and (iii) methods for ontology-based data access. In each category, we cover the main techniques and their background, and compare latest research. Finally, we point out challenges for future work in this research area, which needs a closer integration of Big data and Semantic Web technologies. © 2023, CC BY.

关键词： Big data

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Real-time Non-invasive Blood Glucose Monitoring using Advanced Machine Learning Techniques

Real-time Non-invasive Blood Glucose Monitoring using Advanc...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： L Jenitha Mary V. Vijayashanthi M. Parameswari E. Venitha T A Mohanaprakash S. Dhanush Hariharan Department of CSE DMI College of Engineering Chennai India Department of CSE Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala Engineering College Chennai Tamil Nadu India Department of CSE Kings Engineering College Chennai India Department of CSE Rajalakshmi Institue of Technology Chennai India Department of CSE Panimalar Engineeering College Chennai India Department of Artificial Intelligence and Data Science Panimalar Engineering college Chennai India

When left untreated, diabetes, a chronic ailment that affects a vast number of people overall, might result in major unanticipated problems. The risk of complications can be completely reduced and considerable improvements can be achieved with early detection of diabetes. Recently, the use of wearable technology has emerged as a potential tool for diagnosing and checking illnesses. Smartwatches with bioactive sensors are perfect for diabetes screening because they can provide continuous, painless monitoring of bodily vitals. This paper suggests a methodology for building a hybrid AI model to detect the existence of diabetes using patient data. The system combines body vitals calculated using a smartwatch equipped with a bioactive sensor to provide accurate and continuous information on the wearer’s health state. The hybrid model combines both deep learning and traditional AI computations to achieve a high level of accuracy while diagnosing diabetes. The framework collects data on many bodily parameters, including skin conductance, circulatory strain, and pulse — all of which are known to be strongly associated with diabetes. The acquired data is pre-processed before being utilized to create the hybrid model. The standard AI calculation is used to classify the information into diabetes or non-diabetic categories, while the profound learning calculation is used to eliminate important level highlights from the raw data. The hybrid approach combines the advantages of both deep learning and traditional AI to improve the accuracy of diabetes localization.

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