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Human-Artificial Intelligence Collaboration in HR: Applications and Challenges

Human-Artificial Intelligence Collaboration in HR: Applicati...

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Computational Intelligence and Computing Applications (ICCICA), International Conference on

作者： Neema Gupta Mukesh Joshi Ambuj Kumar Agarwal Muklesh Kumar Tiwari University School of Business Chandigarh University Mohali Punjab India School of Computing Graphic Era Hill University Uttarakhand India Department of Computer Science and Engineering Sharda School of Engineering and Technology Sharda University Greater Noida AIT-Management Chandigarh University

ISBN: (数字)9798350306446

ISBN: (纸本)9798350306453

The alliance between artificial intelligence and human resource professionals is a revolutionary junction that presents both challenging problems and tremendous potential. The applications and challenges of combining human and artificial intelligence in HR are examined in this article. AI is finding its way into HR departments more and more, with uses ranging from improving employee training and hiring procedures to using predictive analytics for workforce planning. The goal of this cooperative synergy is to enhance HR practices' effectiveness, impartiality, and decision-making. However, there are significant challenges to overcome, like Threat to Human Employment, Need for Qualified Applicants, Unethical and Inappropriate Application of Shared Data, Enhancing Employee Turnover between human and AI entities. The paper explores the ever-changing field of HR- related human-AI collaboration, offering insights into emerging applications and challenges that organizations must overcome to realize the full benefits of this revolutionary alliance.

关键词： Training Employment Decision making Collaboration Organizations Distance measurement Planning

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An Analysis of Case Study on Application of Backpropagation Network Using Procedural & Object-Oriented Approach

An Analysis of Case Study on Application of Backpropagation ...

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Emerging Innovations and Advanced Computing (INNOCOMP), International Conference on

作者： Alankrita Aggarwal Pardeep Singh Sonali Gupta Computer Science Engineering Department (AIT) Chandigarh University Mohali India SCSE Galgotias University Greater Noida India Deaprtment of CSE Graphic Era Hill University Research Scholor Graphic Era Deemed to be Univeristy Dehradun India

ISBN: (数字)9798350376470

ISBN: (纸本)9798350376487

The backpropagation (BPN) algorithm is used in artificial neural networks (ANN) for training the networks that uses unsupervised learning and it is performed in two consecutive phases for the training. In the first phase, a pattern is provided as input of network and in response to that activities are performed layers-to-layers on inner layers till a response pattern is generated at the output layer. The second phase basically compares the response pattern generated with the input pattern provided and finds the mismatch that occurred. These mismatches for the target values define the error values and are backpropagated to the input layer as feedback. On the basis of this feedback, the weights for inner layers are adjusted again and again in a loop of backpropagation, such that the final error can be minimized. This work has compared and contrasted the recent behavior between training neural networks using the backpropagation algorithm using a structured & object-oriented approach.

关键词： Training Backpropagation Technological innovation Artificial neural networks Backpropagation algorithms Unsupervised learning

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computer Vision Techniques for Automated Detection of Wildflower Growth Stages

Computer Vision Techniques for Automated Detection of Wildfl...

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Intelligent Computing and Sustainable Innovations in Technology (IC-SIT), International Conference on

作者： Sandhya Pasala Shanker Chandre Balajee Maram Ankit Garg R. Creesy Vignan’sInstitute of Information Technology Department of Computer Science & Artificial intelligence SR University Warangal Telangana School of Computer Science and Artificial Intelligence SR University Warangal Telangana AIT CSE UCRD Chandigarh University Punjab India Department of CSE (AIML) S.A. Engineering College Poonamallee Avadi Road Thiruverkadu Chennai Tamil Nadu India

ISBN: (数字)9798350369175

ISBN: (纸本)9798350369182

This paper presents a new methodology that utilizes computer vision techniques for the automatic detection and classification of stages in wildflower growth. Wild plant development monitoring is essential for ecological studies and biodiversity preservation as well as for environmental management. The approach makes use of novel state-of-the-art computer vision algorithms for the processing of high-resolution images of wildflowers against diverse background scenes. A labeled dataset of wildflower species, at various growth stages, will aid in supervised learning. The system identifies and accurately classifies the growth stage using CNNs and image segmentation algorithms to perform feature extraction. This paper works under computer vision in ecology, thereby simplifying the monitoring of the development processes of wild plant growth while conserving biodiversity.

关键词： Precision agriculture computer vision Technological innovation Supervised learning Vegetation mapping Ecology Classification algorithms Biodiversity Environmental management Monitoring

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Design and Implementation of Object Motion Detection Using Telegram

Design and Implementation of Object Motion Detection Using T...

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2021 International Conference on Technological Advancements and Innovations, ICTAI 2021

作者： Soni, Gaurav Saini, Satnam Singh Malhi, Simarjit Singh Srao, Bhupinder Kaur Sharma, Ashim Puri, Digvijay Apex Institute of Technology Chandigarh University Punjab Mohali India Chandigarh University Department of Computer Science and Engineering Punjab Mohali India Chandigarh University AIT-CSE Punjab Mohali India

ISBN: (纸本)9781665420877

The need for Internet application development is now extremely strong. As a result, the internet of Things (IoT) is a significant technology that allows us to create a variety of valuable internet applications. The Internet of Things (IoT) is an excellent and clever method for reducing human effort and providing simple access to physical objects. With the assistance of different current technologies, these gadgets collect valuable data and exchange it with other devices. Home Automation Systems, for example, utilize Wi-Fi or Bluetooth to transmit data between different home gadgets. Many a time's working parents have to worry about their young children left out alone at home but with advancement in technology it is easier to monitor and transmit information within seconds. Either its about monitoring actions of their children inside house or about visitors at the door it can all be monitored easily with the help of Internet of Things. This hardware based implementation aims at child safety and home security by detecting motion and sending photos on telegram bot helping in detection of any anomaly activity in the absence of owner and with the help of photo it provides the user the opportunity to aware the children at home and, they don't need to open the door for the visitor. This paper gives a overview about the Core implementation Details and the deployment of wireless based control system and accessibility inside a home for authorized people only. When visitor motion is detected at Door, Camera module interfaced to ESP32-CAM used to capture images, save it on system and send it as Telegram alert via TCP/IP. Only authorized user can see the images and can even alert the members inside house. This hardware based design helps in monitoring in place of the owner and helps in saving time of seeing long video clips of CCTV in order to check any unusual activity. The entire control system is built using ESP32-CAM microcontroller and tested for actual use in home environm

关键词： Microcontrollers

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Enhancing Network Security in Distributed Environments using Block Chain-based Solutions

Enhancing Network Security in Distributed Environments using...

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Data science and Information System (ICDSIS), IEEE International Conference on

作者： G Ram Sundar Kapil Rajput S. Dhanasekaran Manisha Aeri Rishi Prakash Shukla Sandeep Kumar Singh Department of Information Technology Sri Ramakrishna Engineering College Coimbatore Tamil Nadu India Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Department of Electronics and Communication engineering Sri Eshwar College of engineering Coimbatore India Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India AIT Management Chandigarh University Mohali India Dr. D. Y. Patil Vidyapeeth-Centre For Online Learning Pune India

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

Block chain generation has been gaining improved attention in recent years because of its ability to enhance the security and performance of community systems. In allotted surroundings, wherein more than one nodes talk with every different, traditional security measures may not be sufficient in preventing malicious assaults or statistics breaches. This is because a centralized authority is often responsible for the safety of the whole community, making it an unmarried point of failure. The block chain era makes use of a decentralized machine, wherein information is stored throughout more than one node and is tested through a consensus mechanism. This eliminates the need for a central authority, offering an extra at-ease and obvious network environment. Additionally, block chain generation permits the implementation of smart contracts, which can automate and put in force security protocols, lowering the capacity for human error.

关键词： Smart contracts Government Force Network security Data science Consensus protocol Protection

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Transfer Learning for Accurate Classification of Breast Cancer in Medical Imaging

Transfer Learning for Accurate Classification of Breast Canc...

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Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering (RMKMATE), International Conference on

作者： R. Sangeetha Rishi Prakash Shukla Satvik Vats Pramod Vishwakarma J. Logeshwaran Department of CSE Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India AIT Management Chandigarh University Gharuan Mohali Punjab India Computer Science & Engineering Graphic Era Hill University Dehradun India CSE (AIT) Chandigarh University Gharuan Mohali Punjab India Department of ECE Sri Eshwar College of Engineering Coimbatore Tamil Nadu India

Transfer learning has recently been developed as a powerful technique for accurate classification of medical images. It is predominantly used in deep learning models to facilitate training of models on small data sets. It is based on the process of leveraging the knowledge gained from prior related tasks and transferring it to a new task. This technique can be used to improve the accuracy of classification models trained on medical images, specifically for the classification of breast cancer. Such models are able to provide an improved accuracy of cancer classification compared with those trained in a standard fashion. Additionally, transfer learning models demonstrate the ability to increase computational efficiency, reduce over fitting, and construct useful representations from data with fewer annotations. This technique is particularly useful for medical imaging due to the expense and difficulty in acquiring large annotated datasets for training purposes. This paper explores the use of transfer learning for accurate classification of breast cancer in medical imaging, and its potential applications in the diagnosis of this disease..

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Analyzing the growing factor of Financial Markets Using Sentimental Analysis Algorithms

Analyzing the growing factor of Financial Markets Using Sent...

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IEEE International Conference on engineering Education: Innovative Practices and Future Trends (AICERA)

作者： Praveen Kantha N. Thiyagarajan Vikrant Sharma J. Logeshwaran Pramod Vishwakarma Chitkara University School of Engineering and Technology Chitkara University Himachal Pradesh India Department of ECE Sri Eshwar College of Engineering Coimbatore India Computer Science and Engineering Graphic Era Hill University Dehradun AIT Chandigarh University Gharuan Mohali Punjab India

Sentiment analysis algorithms have grown to be increasingly famous within the economic markets. Those algorithms process consumer-generated facts to expect market moves through sentiments, including fear, joy, wonder, anger, and belief. By deciphering the public sentiment, traders can cultivate a higher expertise in financial possibilities. Economic growth drives the exploration of ambitions to identify the factors contributing to the growth of financial markets using sentiment analysis algorithms. We administrate an empirical evaluation to observe this relation. To validate our outcomes, we apply supervised gaining knowledge of algorithms and aid Vector Machines, Random Forests, and Logistic Regression to a dataset consisting of sentiment rankings, textual content, and financial indicators. Our findings reveal a robust correlation between sentiment evaluation and the performance of Finance, Financial Services, and Productivity in the marketplace. We conclude that sentiment analysis has the potential to provide well-timed and correct insights into the marketplace, allowing investors to make more excellent, knowledgeable selections.

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An innovative analysis of Leveraging Transfer Learning for Accurate Image Recognition

An innovative analysis of Leveraging Transfer Learning for A...

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Industrial Electronics: Developments & Applications (ICIDeA), IEEE International Conference on-

作者： V.Aravinda Rajan T. Marimuthu Rishi Prakash Shukla Satvik Vats Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil Virudhunagar Tamil Nadu INDIA AIT Management Chandigarh University Mohali Punjab Department of CS&E Graphic Era Hill University Dehradun India

Transfer learning is a powerful technique used to leverage knowledge learned from one domain and transfer it to another domain, reducing the time needed to train a machine learning model. This paper presents a novel approach to apply transfer learning to improve accuracy in image recognition tasks. The authors first use classical transfer learning algorithms to analyze a publicly available image dataset and extract features relevant to the recognition problem. Then, they combine these extracted features with a convolutional neural network (CNN) to improve the classification accuracy of the CNN. The authors evaluate the performance of their approach using multiple datasets, such as MNIST and SVHN, and demonstrate the superiority in terms of high recognition accuracy, low memory requirements and fewer training iterations. The results show that the proposed approach outperforms the baseline CNNs, with an improvement of up to 10%. This approach could be used by research groups and computer vision systems that are concerned with accurate image recognition tasks.

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A Study on the Potential Role of Blockchain in Future Wireless Mobile Networks

A Study on the Potential Role of Blockchain in Future Wirele...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Madan Lal Saini Jeidy Panduro-Ramirez Jesús Padilla-Caballero Archana Saxena Mohit Tiwari Kolipaka Ravi AIT-CSE Chandigarh University Mohali Punjab India Universidad Tecnológica del Perú Postgraduate Department Universidad César Vallejo Uttaranchal Institute of Technology Uttaranchal University India Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Delhi School of Computer Science and Artificial Intelligence SR University Warangal Telangana India

The "Internet of Things" or IoT has recently transformed the established sector into smart infrastructure with 6G data-driven design. Because of their decentralization, straightforwardness, absence of range assets, intrinsic protection and security, absence of interoperability, mystery, and prospering shrewd application regions like IoT Innovation and Industry 4.0, blockchains (BCT) has drawn in a ton of consideration. These facts served as the impetus for this paper's extensive survey, which focused on the potential benefits and difficulties of integrating blockchain technology into 6G cell networks, Industrial iot, and smart industries. These issues included power grid sharing, mathematical loads, response time, transmission capacity above, plans of action, maintainability goals, and edge insight. Specialists accentuated the combination of blockchain and IoT to empower knowledge dispersion in modern IoT later on, as well as the innovation model of 6G to understand the effective execution of BCT plans. This paper talked about the interesting issues that are now being faced, mitigating strategies, and potential future research directions that could aid in the realisation of this vision.

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Machine Learning and AI in Tele-Communication Networks and Iota for Predictive Maintenance

Machine Learning and AI in Tele-Communication Networks and I...

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Data science and Information System (ICDSIS), IEEE International Conference on

作者： S Ponnambili K. Suganyadevi Kapil Rajput Rishi Prakash Shukla Manisha Aeri Sandeep Kumar Singh Department of ECE Mahaguru Institute of Technology Kerala India Department of ECE Sri Eshwar college of engineering Coimbatore Tamil Nadu India Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India AIT Management Chandigarh University Mohali India Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Dr. D. Y. Patil Vidyapeeth- Centre for Online Learning Pune India

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

This has emerge as important tools in the field of tile-communication networks and the net of factors (IoT). These technologies have the ability to analyze large amounts of statistics and make predictions that could significantly enhance the performance and reliability of these networks. One vicinity wherein ML and AI have proved to be mainly beneficial is in predictive protection for tele-communique networks and IoT devices. Predictive maintenance involves using data and algorithms to expect while system or gadgets are likely to fail, taking into account proactive protection to be executed earlier than any foremost issues occur. ML and AI strategies may be carried out to the giant quantities of statistics generated with the aid of tele-verbal exchange networks and IoT devices, consisting of community traffic, sensor readings, and tool performance facts. By way of studying this statistics, those technologies can identify styles and anomalies that can imply capacity device disasters or malfunctions.

关键词： Performance evaluation Machine learning Prediction algorithms Internet of Things Reliability Resource management Object recognition

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