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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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Identifying molecular signatures and pathways shared between Alzheimer's and Huntington's disorders: A bioinformatics and systems biology approach

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Informatics in Medicine Unlocked 2022年 30卷

作者： Mahbub, Nosin Ibna Hasan, Md. Imran Rahman, Md Habibur Naznin, Feroza Islam, Md Zahidul Moni, Mohammad Ali Dept. of Information and Communication Technology Islamic University Kushtia 7003 Bangladesh Dept. of Computer Science and Engineering Islamic University Kushtia 7003 Bangladesh Dept. of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh Dept. of Computer Science and Engineering Pabna University of Science and Technology Bangladesh Artificial Intelligence & Digital Health Data Science School of Health and Rehabilitation Sciences Faculty of Health and Behavioural Sciences The University of Queensland St Lucia 4072 QLD Australia

Alzheimer's disease and Huntington's disease are considered to be the most lethal illnesses that result in common human disorders. Alzheimer's disease (AD) is a progressive neurological illness distinguished by age-related dementia, mental abnormalities, and poor memory, among other symptoms. However, Huntington's disease (HD) is influenced by genetics as well as a generalized dysfunction of the motor system. Despite the fact that many similar genetic elements have been found in the literature as being interrelated between these two diseases, it is still unclear how people acquire infected with these two neurological disorders. Detecting biomarkers for Alzheimer's and Huntington's disease in brain tissue might help in drug development and treatment. The purpose of this research was to find brain cell transcripts that show levels of gene expression linked to the progression of Alzheimer's and Huntington's disease. A bioinformatics pipeline was used to study one RNA-Seq transcriptomic dataset and one microarray dataset, and 24 significant differentially expressed genes (DEGs) were discovered that were shared by two brain cell datasets. We uncovered disease-gene association networks and signaling pathways, as well as gene ontology (GO) investigations and hub protein identification, to determine the roles of these DEGs. The discovery of significant gene ontologies and molecular pathways increased our understanding of the pathophysiology of these two disorders, and the hub proteins B2M, HLA-A, HLA-E, HLA-B, HLA-C, HLA-F, CANX, HLA-DQA1, HLA-DRA, and HLA-DRB1 might be exploited to design therapeutic interventions. In neurological disorder subjects, we uncovered efficient hypothetical linkages between pathogenic processes in brain cells, implying that brain cells may be exploited to detect and monitor illness origin and development, as well as design pharmacological therapies. © 2022

关键词： Alzheimer's disease Biomarker signatures Differentially expressed genes Huntington's disease Protein-drug interactions Protein-protein interaction

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FlowX: Towards Explainable Graph Neural Networks via Message Flows

arXiv

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arXiv 2022年

作者： Gui, Shurui Yuan, Hao Wang, Jie Lao, Qicheng Li, Kang Ji, Shuiwang The Department of Computer Science and Engineering Texas A&M University College StationTX77843 United States The Department of Electronic Engineering and Information Science University of Science and Technology of China Anhui Hefei230026 China The School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China The West China Biomedical Big Data Center West China Hospital Sichuan Chengdu610041 China

We investigate the explainability of graph neural networks (GNNs) as a step toward elucidating their working mechanisms. While most current methods focus on explaining graph nodes, edges, or features, we argue that, as the inherent functional mechanism of GNNs, message flows are more natural for performing explainability. To this end, we propose a novel method here, known as FlowX, to explain GNNs by identifying important message flows. To quantify the importance of flows, we propose to follow the philosophy of Shapley values from cooperative game theory. To tackle the complexity of computing all coalitions’ marginal contributions, we propose a flow sampling scheme to compute Shapley value approximations as initial assessments of further training. We then propose an information-controlled learning algorithm to train flow scores toward diverse explanation targets: necessary or sufficient explanations. Experimental studies on both synthetic and real-world datasets demonstrate that our proposed FlowX and its variants lead to improved explainability of GNNs. Copyright © 2022, The Authors. All rights reserved.

关键词： Message passing

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Mobile Application for Water Management and Monitoring System in Residential Building

Mobile Application for Water Management and Monitoring Syste...

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International Conference on Futuristic Technologies (INCOFT)

作者： Nalla Akhila Aswadhati Sirisha P. Dass P M Dinesh T Sujatha Amit Kamalakar Gaikwad Computer Science and Engineering Aditya Engineering College Surampalem Jawaharlal Nehru technological university Kakinada Kakinada East Godavari district Information Technology (IT) Department Vignans institute of information technology (A) Duvvada India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Department of ECE Sona College of Technology Salem Department of Artificial Intelligence and Data Science Sri Krishna College of Engineering and Technology Coimbatore Department of Computer Science G.H.Raisoni University Amravati Maharashtra

Water scarcity is one of the major threats to human society. Though there are many reasons for water scarcity like water pollution, drought and global warming, overuse of domestic water is one of the major causes of water scarcity. More than one billion people are deprived of clean drinking water and water for other purposes. One of the main solutions that were found to this problem is rainwater harvesting. Many countries encouraged the citizens to collect the rainwater for further uses. Though the rainwater is preserved, another issue named water over usage comes into existence. Most of the time, the water supplied for domestic reasons may undergo several preprocessing like filtration, osmosis, etc. to ensure the cleanliness of the water. When this water is wasted for petty reasons like washing dishes and watering plants, it may lead to the shortage of freshwater for necessary needs like cattle feeding, food production, farming, etc. Many researchers and scientists have found a lot of techniques and processes to minimize the usage of fresh water in households. This project aims the development of a mobile application that constantly monitors the water usage by a house in a community. There will be a prefixed value for the amount of water each house can use. If the water usage exceeds the prefixed value, the inmates of the house are asked to pay a certain fine amount which is calculated by the number of extra liters used and the prefixed fine amount for each liter. If the fine amount was not paid, the water supply to that particular house will be blocked. All of the above-mentioned processes are monitored by this application. Hardware components like multiple sensors, adaptors, motors are used for this purpose. The whole working of the hardware is controlled by a microcontroller namely Arduino Uno. This application helps in the control of the over usage of water in residential buildings. This can be equipped in apartments, community houses, quarters, etc. to constan

关键词： Microcontrollers Buildings Production Water pollution Hardware Osmosis Sensors

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Curriculum pre-training heterogeneous subgraph transformer for top-N recommendation

arXiv

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arXiv 2021年

作者： Wang, Hui Zhou, Kun Zhao, Wayne X.I.N. Wang, Jingyuan Wen, Ji-Rong School of Information Renmin University of China Gaoling School of Artificial Intelligence Renmin University of China Beijing Key Laboratory of Big Data Management and Analysis Methods School of Computer Science and Engineering Beihang University

Due to the flexibility in modelling data heterogeneity, heterogeneous information network (HIN) has been adopted to characterize complex and heterogeneous auxiliary data in top-N recommender systems, called HIN-based recommendation. HIN characterizes complex, heterogeneous data relations, containing a variety of information that may not be related to the recommendation task. Therefore, it is challenging to effectively leverage useful information from HINs for improving the recommendation performance. To address the above issue, we propose a Curriculum pre-training based HEterogeneous Subgraph Transformer (called CHEST) with new data characterization, representation model and learning algorithm. Specifically, we consider extracting useful information from HIN to compose the interaction-specific heterogeneous subgraph, containing both sufficient and relevant context information for recommendation. Then we capture the rich semantics (e.g., graph structure and path semantics) within the subgraph via a heterogeneous subgraph Transformer, where we encode the subgraph with multi-slot sequence representations. Besides, we design a curriculum pre-training strategy to provide an elementary-to-advanced learning process, by which we smoothly transfer basic semantics in HIN for modeling user-item interaction relation. Extensive experiments conducted on three real-world datasets demonstrate the superiority of our proposed method over a number of competitive baselines, especially when only limited training data is available. Copyright © 2021, The Authors. All rights reserved.

关键词： Information services

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An efficient face recognition attack method based on generative adversarial networks and cosine metrics

An efficient face recognition attack method based on generat...

引用

artificial intelligence Technology (ACAIT), Asian Conference on

作者： Hu Ding Shumeng He Yanwen Wu Yongli Jin Lin Gan Gaodi Xu Houqun Yang School of Computer Science and Technology Hainan University Haikou Hainan School of Big Data and Software Engineering Chongqing University Chongqing School of Computer Science and Technology Anhui University Hefei Anhui Elmore Family School of Computer and Electrical Engineering Purdue University West Lafayette USA Hainan Association for Artificial Intelligence Haikou Hainan

ISBN: (纸本)9781665426312

Deep neural networks are vulnerable to attacks on adversarial samples. These attacks are caused by adding small magnitude perturbations to the input samples, which may lead to misclassification of the deep neural network. Based on the study of the adversarial sample attack network model, we propose an attack sample based on the generative adversarial network HNUGAN, incorporating the cosine metric of disparity recognition, for the features of the face dataset, to construct an attack sample to attack the face recognition system. Using these adversarial samples can reduce the recognition accuracy of models such as GoogleNet and ResNet to a very low level, and thus complete the attack on the target model. Interestingly, we can obtain a batch of adversarial samples through adversarial training to expand the dataset and retrain the target model to improve its robustness and resistance to attacks.

关键词： Training Measurement Deep learning Resistance Target recognition Face recognition Perturbation methods

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Information Fusion in Smart Agriculture: Machine Learning Applications and Future Research Directions

arXiv

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arXiv 2024年

作者： Katharria, Aashu Rajwar, Kanchan Pant, Millie Velásquez, Juan D. Snášel, Václav Deep, Kusum Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Roorkee Roorkee India Statistical Quality Control and Operations Research Unit Indian Statistical Institute Hyderabad India Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Roorkee Roorkee India Department of Industrial Engineering University of Chile Santiago Chile Instituto Sistemas Complejos de Ingeniería Santiago Chile Department of Computer Science VSB-Technical University of Ostrava Ostrava Czech Republic Department of Mathematics Indian Institute of Technology Roorkee Roorkee India

Machine learning (ML) is a rapidly evolving technology with expanding applications across various fields. This paper presents a comprehensive survey of recent ML applications in agriculture for sustainability and efficiency. Existing reviews mainly focus on narrow subdomains or lack a fusion-driven perspectives. This study provides a combined analysis of ML applications in agriculture, structured around five key objectives: (i) Analyzing ML techniques across pre-harvesting, harvesting, and post-harvesting phases. (ii) Demonstrating how ML can be used with agricultural data and data fusion. (iii) Conducting a bibliometric and statistical analysis to reveal research trends and activity. (iv) Investigating real-world case studies of leading artificial intelligence (AI)-driven agricultural companies that use different types of multisensors and multisource data. (v) Compiling publicly available datasets to support ML model training. Going beyond existing previous reviews, this review focuses on how machine learning (ML) techniques, combined with multi-source data fusion (integrating remote sensing, IoT, and climate analytics), enhance precision agriculture by improving predictive accuracy and decision-making. Case studies and statistical insights illustrate the evolving landscape of AI driven smart farming, while future research directions also discusses challenges associated with data fusion for heterogeneous datasets. This review bridges the gap between AI research and agricultural applications, offering a roadmap for researchers, industry professionals, and policymakers to harness information fusion and ML for advancing precision agriculture. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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STRIDERNET: A GRAPH REINFORCEMENT LEARNING APPROACH TO OPTIMIZE ATOMIC STRUCTURES ON ROUGH ENERGY LANDSCAPES

arXiv

引用

arXiv 2023年

作者： Bihani, Vaibhav Manchanda, Sahil Sastry, Srikanth Ranu, Sayan Krishnan, N.M. Anoop Department of Civil Engineering Indian Institute of Technology Delhi Hauz Khas New Delhi110016 India Department of Computer Science and Engineering Indian Institute of Technology Delhi Hauz Khas New Delhi110016 India Theoretical Sciences Unit School of Advanced Materials Jawaharlal Nehru Centre for Advanced Scientific Research Rachenahalli Lake Road Bengaluru560064 India Yardi School of Artificial Intelligence IIT Delhi India

Optimization of atomic structures presents a challenging problem, due to their highly rough and non-convex energy landscape, with wide applications in the fields of drug design, materials discovery, and mechanics. Here, we present a graph reinforcement learning approach, STRIDERNET, that learns a policy to displace the atoms towards low energy configurations. We evaluate the performance of STRIDERNET on three complex atomic systems, namely, binary Lennard-Jones particles, calcium silicate hydrates gel, and disordered silicon. We show that STRIDERNET outperforms all classical optimization algorithms and enables the discovery of a lower energy minimum. In addition, STRIDERNET exhibits a higher rate of reaching minima with energies, as confirmed by the average over multiple realizations. Finally, we show that STRIDERNET exhibits inductivity to unseen system sizes that are an order of magnitude different from the training system. © 2023, CC BY.

关键词： Reinforcement learning

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A two-stage deep learning-based hybrid model for daily wind speed forecasting

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Heliyon 2025年第1期11卷 e41026页

作者： Band, Shahab S. Ameri, Rasoul Qasem, Sultan Noman Mehdizadeh, Saeid Gupta, Brij B. Pai, Hao-Ting Shahmirzadi, Danyal Salwana, Ely Mosavi, Amir Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Computer Science Department College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh 11432 Saudi Arabia Computer Science Department Faculty of Applied Science Taiz University Taiz 6803 Yemen Water Engineering Department Urmia University Urmia Iran Department of Computer Science and Information Engineering Asia University Taichung 413 Taiwan Symbiosis Centre for Information Technology (SCIT) Symbiosis International University Pune India Center for Interdisciplinary Research University of Petroleum and Energy Studies (UPES) Dehradun India Department of Big Data Business Analytics National Pingtung University Pingtung Taiwan of Engineering Science and Technology National Yunlin University of Science and Technology 123 University Road Yunlin Douliou 64002 Taiwan Institute of Visual Informatics Universiti Kebangsaan Malaysia Selangor Bangi 43600 Malaysia John von Neumann Faculty of Informatics Obuda University Budapest Hungary Ludovika University of Public Service Budapest Hungary

Global adoption of wind energy continues to increase, while improving the efficiency of turbine settings requires reliable wind speed (WS) models. The latest models rely on artificial intelligence (AI) optimizations which constructs tests on a range of novel hybrid models to examine the reliability. Gradient Boosting (GB), Random Forest (RF), and Long Short-Term Memory (LSTM) are used in new combinations for data pre-processing. A Time Varying Filter-based Empirical Mode Decomposition (TVFEMD) model is coupled with the GB and LSTM standalone models, to create TVFEMD-GB and TVFEMD-LSTM hybrids, which are run in competition with each other. Eventually, a preferred hybrid form is established, simultaneous hybridization of TVFEMD with GB and LSTM. This study is the first to hybridize these fundamental systems, and create a TVFEMD-GB-LSTM model that can forecast WS. This study finds that the novel hybrid models exhibit superior performance to standalone GB and LSTM models, opening the pathway to alternative WS prediction techniques. © 2024 The Authors

关键词： artificial intelligence Big data data science Forecasting Gradient boosting Long short-term memory Machine learning Wind speed

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IoT Based Fish Pond Monitoring System to Enhance Its Productivity

IoT Based Fish Pond Monitoring System to Enhance Its Product...

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Innovative Practices in Technology and Management (ICIPTM), International Conference on

作者： S M Ravi Kumar Thayyaba Khatoon Mohammed Seeram Srinivasa Rao Dinesh Anton Raja P Ruhi Bakhare Ashok Kumar Swagata Sarkar Department of Mechanical G. Pullaiah College of Engineering and Technology Andhra Pradesh India School of Engineering Malla Reddy University Telangana India Department of Mechanical Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh India Department of Electronics & Communication R.M.K. Engineering College Chennai India Department of Master of Business Administration Dr. Ambedkar Institute of Management Studies & Research Maharashtra India Department of Computer Science Banasthali Vidyapith Rajasthan India Department of Artificial Intelligence & Data Science Sri Sai Ram Engineering College Chennai India

Modernizing fish ponds for agricultural production face major challenges in terms of capital expenditure and operational expenses. Fish farming of particular types of fish species necessitates the fulfilment of several requirements because, like several other living organisms, fish have a precise limit for an assortment of environmental criteria. For the good health and development of the organisms, big farms typically have certain kinds of water surveillance and replacement mechanization systems. To preserve the ecosystems for living fish, the people who work in the fish farming ponds must be active throughout the day. Local farmers who operate on relatively small ponds couldn't afford to compensate employees to manage daily tasks, which typically include keeping an eye on water levels, temperature, and pH levels. As a result, the prime motive for this paper is to monitor and take steps to keep the habitat's eco-friendly environment for specific species of fish, which will decrease the time required for some basic actions. This article puts forth a smart Internet of Things (IoT) based fish pond water monitoring system. Such a smart system consists of several real-time sensors that monitor and send inputs to a microcontroller and the data is stored in a real-time database. The user can track these values through a phone app that is assimilated with the cloud by having them transmitted to the cloud at periodic intervals and this helps the fish pond owners to take required action quickly and effectively when needed. As embedding devices are typically made up of an Arduino board, internet and relay frames, and a computer interface, a farmer could easily source these parts. With the help of this integration system, farmers can reduce operating costs and boost overall effectiveness by avoiding the need to hire employees for their location.

关键词： Temperature sensors Temperature measurement Productivity Cloud computing Microcontrollers Fish Real-time systems

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