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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Gupta, Arhaan Kumar, Kirana Asha, V. Kumar, S. Naveen Modern School Barakhamba Road New Delhi India Institute of Aeronautical Engineering Department of Electronics and Communication Engineering Telangana Hyderabad India New Horizon College of Engineering Department of Master of Computer Application Karnataka Bangalore India Mlr Institute of Technology Department of Electronics and Communication Engineering Telangana Hyderabad India

ISBN: (纸本)9798350350067

Economic experts investigate links between economic entities by analyzing large volumes of data. Econometrics is the process of using statistical methods on economic data to find these links through independent observations. Conventional econometric techniques evaluate economic data using only mathematical and statistical concepts. Conversely, applied econometrics concentrates on real-world observations, like economic results, market price swings, and forecasts. In recent years, Machine Learning models have become increasingly popular across numerous fields, including economics. When compared to typical econometric models, these models provide more accurate analysis of economic data and have shown to be more efficient in tasks like classification. This study examines some machine learning techniques are especially utilized with economic data. It also explores metrics for econometric model analysis and supervised machine learning techniques. The paper explores the possible future uses of the machine learning models that Economic experts find most appealing. © 2024 IEEE.

关键词： Adversarial machine learning

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Examining Driver Sleepiness Detection Based on Visual Actions Using Haar Cascading and CNN

Examining Driver Sleepiness Detection Based on Visual Action...

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Disruptive Technologies (ICDT), International Conference on

作者： Vandana Rawat Pratibha Manral Lakshya Gupta Nidhi Joshi Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Application Graphic Era Deemed to be University Dehradun India Department of Computer Application Graphic Era Hill University Dehradun India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Every hour, there are numerous road accidents all over the world. Some of these accidents are caused by drunk driving, lack of sleep, inattention behind the wheel, and a variety of other factors that can be dangerous for both passengers and other road users. The most frequent circumstance is sleeping deprivation, which can cause the driver to be reckless while operating a motor vehicle. Driver drowsiness detection systems are particularly effective in identifying drowsiness by measuring the size of the driver's eyes and the rate at which they blink using a camera and software that is connected to it. The driver fatigue detection system works utterly offline, leveraging the Haar Cascade and Perclos machine learning methodologies, and can warn the driver via alarm if he or she is feeling drowsy.

关键词： Training Visualization Accuracy Sleep Lighting Wheels Software Software measurement Vehicles Testing

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Examining the Advantages of Dynamic Communications Software Design in Networks 5th

Examining the Advantages of Dynamic Communications Software ...

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5th International Conference on Data Science, Machine Learning and applications, ICDSMLA 2023

作者： Raghavendra, R. Das, Neeraj Inzimam Kurmi, Raviraj Singh Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Application Vivekananda Global University Jaipur India Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9789819780426

This paper examines the blessings of dynamic communications software program design in networks. By leveraging advances in generation, dynamic communications provide an ever-evolving method of connecting, communicating, and sharing facts inside networks without relying on static, often outdated, hardware. The principle blessings related to this community layout are scalability, flexibility, and excellent reliability. As networks extend, dynamic communications software can auto-regulate accordingly to ensure the overall capability of the general gadget. Moreover, users can count on operational performance upgrades and extended capability to address protection and privacy worries. Moreover, using dynamic software allows for more green aid usage, which facilitates lessening expenses related to the control and operation of the network. Moreover, dynamic communications have also helped reduce downtime and sell extra resilience in the face of surprising changes or unanticipated threats. Standard, this paper provides an in-depth knowledge of the advantages of dynamic communications software layout in networks. Dynamic Communications software program design and its various benefits in networking have been gaining substantial attention in recent years. Dynamic Communications software program layout additionally allows protocols to be modified on an as-needed foundation. That is useful for networks that might be constantly changing and want to adapt, which will take care of the converting environment. New protocols may be introduced or eliminated as wished, and the gadget will adjust to make the most efficient performance. In addition, using Dynamic Communications software design allows for a more straightforward integration of different systems. It is useful for networks that require a couple of systems to work together in order for them to function well. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Software design

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Real-Time Data Streams: The Future of Predictive Maintenance in Manufacturing 3

Real-Time Data Streams: The Future of Predictive Maintenance...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Goyal, Manish Kumar Haripriya Singh, Akhilendra Pratap Vivekananda Global University Department of Computer Science & Application Jaipur India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350343274

Dynamic Predictive Modeling is a method that has brought about a revolutionary change in the field of preventive maintenance in the industrial sector. Using the benefits of real-time data streams and flexible algorithms, this cutting-edge technology enhances predictive maintenance processes. In this investigation, we evaluate the efficacy of Dynamic Predictive Modeling to that of numerous more conventional methods, including vibration analysis, acoustic emission analysis, thermography, oil analysis, ultrasonic testing, and data analytics. Accuracy, resource optimization, adaptability, downtime reduction, cost-effectiveness, and preventative maintenance are just a few of the criteria we use to evaluate the methods. The findings indicate that Dynamic Predictive Modeling outperforms its predecessors across a range of performance metrics. It can better adapt to the ever-changing industrial environment, allowing for more precise and timely breakdown forecasts. The allocation of resources and the economy of operation are both enhanced by the fact that maintenance procedures may be adjusted according to the actual condition of the equipment. Predictive maintenance is the most effective method for minimizing unscheduled downtime and increasing output in heavy industry. In a comparison of several approaches to predictive maintenance, Dynamic Predictive Modeling comes out on top with the highest aggregate score. A study reveals how an unconventional approach might improve the effectiveness and reliability of industrial operations. Increased equipment reliability, decreased maintenance costs, and a potential advantage in the manufacturing industry might arise from the use of dynamic predictive modeling. © 2023 IEEE.

关键词： Cost effectiveness

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Profiling Industrial 3D Parts with Complex Shapes Using 2D Imagery 15

Profiling Industrial 3D Parts with Complex Shapes Using 2D I...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Mahato, Nivedan Kaushik, Harshita Swadimath, Uma C. Arka Jain University Department of Mechnical Engineering Jharkhand Jamshedpur India Vivekananda Global University Department of Computer Science & Application Jaipur India School of Mangement - Pg Department of Management Karnataka Bangalore India

ISBN: (纸本)9798350370249

Commercial 3-D element profiling via second imagery is a modern technique to acquire the 3-D geometric characteristics of quite complicated shapes frequently encountered in industrial additives. This approach makes use of a set of pix taken from multiple angles to derive the corresponding 3-D profile of the component with the use of photogrammetry strategies. The resulting three-D statistics can be similarly characterized to evaluate the part's dimensional accuracy, floor texture, and different attributes. The novel technique described in this work augments conventional measuring methods and opens new possibilities for automated bodily asset evaluation, reverse engineering, and automated inspection. The method defined has potential programs in an extensive type of fields in which correct geometrical characterization of complex parts is important. © 2024 IEEE.

关键词： Characterization Commercial complex Complicated geometrical Photogrammetry Potential

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Real-Time Connectivity with the Industrial Internet of Things 15

Real-Time Connectivity with the Industrial Internet of Thing...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Jayakumar, Sujayaraj Samuel Kumar, Ashwini Kharbas, Vikas Kumar School of Sciences Department of Forensic Science Karnataka Bangalore India Arka Jain University Department of Mechnical Engineering Jharkhand Jamshedpur India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350370249

The economic net of things (Eliot) is an emerging era permitting the relationship and optimization of devices, machines, and belongings in industrial settings via statistics switch and exchange. Real-time connectivity with the Iota has revolutionized how business systems perform and has opened the door for full-size upgrades in productiveness, performance, and protection. With actual-time connectivity, commercial operations were capable of advantage near real-time perception into gadget performance, surroundings conditions, and the behavior of machines and property. It interprets into improved situational cognizance, higher decision-making main to optimized operational processes, and stepped forward usual operational visibility. Furthermore, actual-time connectivity with the Eliot is likewise allowing superior information analytics capabilities, taking into account the improvement of predictive analytics fashions, optimization of plant operations, and the gathering of critical information factors. All of those traits have improved the efficiency, scalability, and reliability of business operations and networks, permitting companies to higher-utilize their assets and become greater efficient. © 2024 IEEE.

关键词： Predictive analytics

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Cascade Temporal Convolutional Network for Multitask Learning 6

Cascade Temporal Convolutional Network for Multitask Learnin...

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6th International Conference on Artificial Intelligence and Big Data, ICAIBD 2023

作者： Zhang, Huiyun Huang, Heming Han, Henry Baylor University School of Engineering and Computer Science Department of Computer Science United States Qinghai Normal University School of Computer Science Xining China Qinghai Normal University The State Key Laboratory of Tibetan Intelligent Information Processing and Application School of Computer Science Xining China

ISBN: (纸本)9781665491259

Multi-task learning (MTL) is a powerful technique in machine learning that enables the simultaneous training of multiple tasks. In this paper, we propose a novel MTL approach for the recognition of emotions, speakers, and gender in speech signals. Our approach presents a novel deep learning model called the Cascade Temporal Convolutional Network (CTCN to handle MTL efficiently. CTCN is designed to effectively improve the parallel processing ability of the model in multitasking learning. It provides an effective solution for improving the performance of deep learning in multitasking scenarios. CTCN is particularly adept at processing sequences of data with varying lengths and extracting features that are relevant to multiple tasks. Our experimental results demonstrate that the proposed CTCN approach for MTL achieves significantly superior performance on all tasks on benchmark datasets compared to other deep learning models. This study will inspire more future work in the MTL field. © 2023 IEEE.

关键词： Speech recognition

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First Principles Study of CoSb_(3)/Ni Interface Structure and Mechanical Properties

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Acta Metallurgica Sinica(English Letters) 2025年第5期38卷 793-802页

作者： Cuicui Shu Pengcheng Zhai Xiege Huang Sergey IMorozov Guodong Li Zhiyuan Pan Hubei Key Laboratory of Theory and Application of Advanced Materials Mechanics Wuhan University of TechnologyWuhan 430070China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of TechnologyWuhan 430070China Department of Computer Simulation and Nanotechnology South Ural State UniversityChelyabinsk 454080Russia School of Civil Engineering Wuhan Huaxia Institute of TechnologyWuhan 430223China

Mechanical stability is critically essential in the design of thermoelectric *** this study,we employed first-principles calculations based on density functional theory to investigate the failure mechanisms at the CoSb_(3)/Ni *** findings reveal that the CoSb_(3)(100)/Ni(100)and CoSb_(3)(100)/Ni(111)_1 configurations are favorable interface *** ideal tensile strength of the CoSb_(3)/Ni interface is markedly lower than that of bulk CoSb_(3),which can be attributed to structural rearrangements near the interface that weaken the strength of the Co–Sb *** failure occurs in CoSb_(3),where covalent Sb–Sb bonds exhibit a tendency to soften prior to the ionic Co–Sb bonds due to their comparatively lower ***,the breakage of the Co–Sb bonds leads to interface *** failure at both single-layer Sb_CoSb_(3)(100)/Ni(100)and single-layer Sb_CoSb_(3)(100)/Ni(111)_1 interfaces results from ruptures in intermediate Co–Sb bonds in CoSb_(3),whereas failures at double-layer Sb_CoSb_(3)(100)/Ni(100)and double-layer Sb_CoSb_(3)(100)/Ni(111)_1 interfaces stem from fractures in the uppermost Co–Sb *** behavior is primarily driven by atomic rearrangements near the single-layer Sb_CoSb_(3)interface,which promote bond formation between Sb–Ni and Co–Ni,thereby enhancing stability within the superstructure of CoSb_(3).This study will provide a theoretical basis for the interface design of thermoelectric devices.

关键词： Thermoelectric Intercfacial structure Ideal tensile strength Failure mechanism

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Disease Detection in C4 Plants using Machine Learning Approaches 2

Disease Detection in C4 Plants using Machine Learning Approa...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Singh, Souravdeep Sharma, Geeta Kumar, Ram Kumar, Adesh School of Computer Application Lovely Professional University Punjab Phagwara144411 India School of Computing Science and Engineering VIT Bhopal University Bhopal India School of Agriculture Lovely Professional University Phagwara India

ISBN: (纸本)9798331519056

Disease detection in crops is essential to maintaining food security and agricultural productivity. Plant disease identification is essential to maintaining crop health and increasing agricultural productivity. with the advancement of computational techniques like Machine learning is used for extracting insights from data, enabling automation, and making predictions in various farming domains. Recent assessments reveal that infections that develop on the leaves of plants are causing significant losses for 56% of the agriculture sector. Monitoring the disease's growth and increasing agricultural production is essential. We must first identify the condition early on and take action to stop it from propagating. Therefore, we could potentially able to resolve this challenge by implementing some algorithms for identifying diseases on leaflets. In this study, we investigate the efficacy of Convolutional Neural Networks (CNNs) for detecting diseases in two major crops: maize and sugarcane which lie under C4 plants. Utilizing a dataset located from Kaggle, comprising a diverse range of images depicting various disease symptoms, we trained and evaluated CNN models for both crops. The accuracy of disease detection achieved remarkable results, with 98% accuracy for maize and 100% accuracy for sugarcane. Our comparative analysis highlights the efficacy of CNNs in accurately identifying diseases in these crops, showcasing their potential for aiding farmers in timely disease management and enhancing agricultural yield. © 2024 IEEE.

关键词： C4 Plants Leaf diseases Machine Learning Maize Sugarcane

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Smart Home Personal Assistants: Fueled by Natural Language Processor and Blockchain Technology 2

Smart Home Personal Assistants: Fueled by Natural Language P...

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2nd International Conference on Interdisciplinary Cyber Physical Systems, ICPS 2022

作者： Ansar, Syed Anas Jaiswal, Kriti Aggarwal, Shruti Shukla, Shruti Yadav, Jaya Soni, Nupur Babu Banarasi das University Department of Computer Application Lucknow India University of Lucknow Department of Computer Science Lucknow India Thapar Institute of Engineering and Technology Department of Computer Science and Engineering Patiala India

ISBN: (数字)9781665470223

ISBN: (纸本)9781665470223

With the ever-increasing cost of livelihood, technological intervention is necessary to keep costs under control. Hence, smart home technology can be considered as a well-established and entrenched region of interest. In the current scenario, research subsidizes present-time houses, enabling the user to design and manage a smart home to conserve energy while allowing for more autonomous applications. A voice-activated home automation system integrates the Internet of Things, Artificial Intelligence, Blockchain, and Natural Language Processing to deliver a cost-effective and efficient method of interacting with household equipment. The primary objective of this paper is to assess SPAs using blockchain technology and a natural language processor (NLP), as well as the design of a SPA based on the internet of things. Applicable cyber threats and countermeasures are also addressed in this article. Using Yule's coefficient of association, a case study was undertaken to evaluate the association between privacy and security threats posed by this modern technology. The findings show that privacy and security are positively related. © 2022 IEEE.

关键词： Blockchain

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