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Human-cantered Artificial intelligence in Education, Present and future opportunities 3

Human-cantered Artificial intelligence in Education, Present...

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3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Mittal, Arun Ramachandran, K.K. Lakshmi, K.K. Hasbullah, Nornajihah Nadia Ravichand, M. Lourens, Melanie Birla Institute of Technology Department of Management Uttar Pradesh India Dr G R D College of Science Management/Commerce/International Business India Dwaraka Doss Goverdhan Doss Vaishnav College The Department of English India Faculty of Business and Management Melaka Malaysia Mohan Babu University School of Liberal Arts and Allied Sciences India Durban University of Technology Faculty of Management Sciences South Africa

ISBN: (纸本)9798350399264

Artificial intelligence (AI), which was once only a pipe dream, is now a reality, permeating every facet of our life - including education - and becoming a regular part of our daily existence. Although it is still a young field, as time goes on, we shall see how AI develops and discover its unrealized potential. In light of the foregoing, this chapter explores the most recent developments in AI and its potential applications in a variety of fields, including Natural language, computer vision, and supervised learning. To achieve this, networking websites analysis (SNA) is used as a basis for the comprehension of the most central themes in Academic research from the perspective of education. Three main topics arose from the investigation: (1) individualized training, learning styles, and adaptive learning;(2) advanced tutoring systems and adviser systems;and (3) intelligence as a possible improvement to educational institutions, practices in the future. Benefits of e - learning (LMS) offer the best alternative for supplying teaching programs in higher education by giving students from all across the world top-notch resources. As a bridge, student-teacher learning management programmes are utilized. This paradigm is altered by intelligent agents who emulate the clever strategies employed by the teacher and the pupils. Ai - powered technologies are constructed on the duplication of principles that are necessary for human understanding. This analysis includes the analysis of the Modules that utilize AI methods. The emphasis also includes more instances of the huge variation. Learning administration system techniques related to AI have been researched to help students, faculty and staff, and administrations and to overcome some LMS issues. © 2023 IEEE.

关键词： E-learning

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Comparison of Various Classifiers for Efficient Plant Diseases detection Using Image Processing Techniques 3

Comparison of Various Classifiers for Efficient Plant Diseas...

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3rd international conference on Advancement in Electronics and Communication Engineering, AECE 2023

作者： Garg, Rinku Sharma, Ashish Babbar, Atul Goyal, Vishal Waheeb, Khlood Gehlot, Anita University Institute of Computing Chandigarh University Mohali India Chandigarh University Department of Electrical Engineering Mohali India Sgt University Department of Mechanical Engineering Gurugram India Gla University Department of Ece Mathura India Medical Technical College Al-Farahidi University Iraq Uttaranchal Institute of Technology Uttaranchal University Department of Ece Dehradun India

ISBN: (纸本)9798350330724

Processing images of the fruit, the leaves, or the entire plant, image processing enables the early diagnosis of fruit illnesses. In order to stop the spread of illnesses and reduce crop losses, early diagnosis is essential. Image processing techniques can identify subtle changes in color, texture, and shape that are often indicative of disease symptoms. This level of precision and accuracy is challenging to achieve through human visual inspection alone. Image processing provides an objective and consistent analysis of disease symptoms. Human judgment can be influenced b y various factors, whereas i mage processing algorithms follow predefined criteria, reducing s ubjectivity, To create predictive models, image processing may be combined with other technologies like machine learning and artificial intelligence (AI). Based on past data and environmental factors, these models can predict disease outbreaks. In this work we are discussing various existing image processing techniques that will predict the diseases in fruit efficiently and accurately. © 2023 IEEE.

关键词： Diseases

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Lane detection algorithm based on multi-head self-attention and multilevel feature fusion 3

Lane detection algorithm based on multi-head self-attention ...

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3rd international conference on Artificial intelligence and Computer Engineering, ICAICE 2022

作者： Guo, Bobo Qiang, Zanxia Bao, Xianfu Xu, Yao Faculty of Computer Science Zhongyuan University of Technology Henan Zhengzhou China Faculty of Artificial Intelligence Hangzhou Dianzi University Zhejiang Hangzhou China

ISBN: (纸本)9781510663473

Lane detection is a crucial environmental sensing technique that is used in advanced driving assistance systems and automatic driving. The research on this issue has significant practical value. Aiming the current lane detection algorithm could not solve the problems of the local receptive field and detail feature loss, we introduced the multi-head self-attention module in Transformer into the encoder and decoder to obtain the global receptive field while solving the problem of detail feature loss with the multi-level feature fusion decoder. The proposed algorithm has been compared with the ERFNet model in the CULane dataset, and the detection accuracy has improved by 3.9 percentage points. The detection accuracy in the Tusimple dataset is 96.51%.Introducing a multi-head self-attention module increases the feature selection effect of the attention mechanism in the coding and decoding process. It provides a new solution for the lane detection algorithm. © 2023 SPIE.

关键词： Feature extraction

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Assessing the Ability of AI-Driven Natural Language Processing to Accurately Analyze Unstructured Text Data 3

Assessing the Ability of AI-Driven Natural Language Processi...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Vigenesh, M. RaviKumar, V. Patil, Vivek D. Kumar, Sharma Sonu Geyi, Nyarik Chattopadhyay, Subir Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Mangalayatan University Department of Computer Science Aligarh India Himalayan University Department of Bussiness & Management Itanagar India Usha Martin University Faculty of Business Mangagement & Commerce Ranchi India

ISBN: (纸本)9798350319125

This technical abstract examines the ability of herbal language processing (NLP)-driven synthetic intelligence (AI) to analyze unstructured textual content facts. Unstructured textual content records include information from assets with webpages, social media, emails, and consumer comments. They are widely used in sentiment evaluation, hazard detection, and entity extraction programs. NLP-pushed AI answers along with natural language knowledge (NLU), herbal language knowledge (NLG), and natural language understanding (NLP) are used to manner and examine unstructured textual content information. The analysis technique starts evolving with tokenizing the facts, breaking the textual content into smaller factors, including phrases and phrases. After tokenizing the statistics, the AI device discovers styles inside the records. Those styles can then discover the emotions and entities inside the data. NLU and NLG models are used to expand the facts' expertise by assigning sentiment and issue-level scores. The accuracy of NLP-driven AI is similarly progressed through more excellent complex fashions consisting of deep studying and reinforcement. © 2023 IEEE.

关键词： extraction phrases statistics tokenized webpages

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Auction Mechanism Based Multi-agent DRL for Multi-service Placement in Mobile Edge Computing 3

Auction Mechanism Based Multi-agent DRL for Multi-service Pl...

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3rd international conference on Information Communication and Software Engineering (ICICSE)

作者： Yuan, Qifeng Tan, Jian Li, Jiangming Xu, Zhenghuan Chen, Shuangwu Yang, Jian Univ Sci & Technol China Hefei Peoples R China Univ Sci & Technol China Inst Artificial Intelligence Hefei Comprehens Natl Sci Ctr Hefei Peoples R China

ISBN: (纸本)9798350313048

The Mobile Edge Computing (MEC) is proposed as an emerging paradigm for reducing the service latency and saving transmission bandwidth. Service-based application development greatly increases extensibility and maintainability. Compared with the cloud center, the MEC servers have limited computing and storage capabilities, which increases the difficulty of service deployment in the edge network. Since the MEC is a typical distributed system, each server independently updates the service deployment according to geographic differences. In order to improve the system utility, the cooperation between servers is indispensable. In this paper, we combine the multi-agent Deep Reinforcement Learning (DRL) with auction mechanism, aiming to solve the multi-service placement problem in MEC systems, where each server makes the service bid and update decision based on the DRL, and the auction mechanism is used to coordinate the service placement between servers. The simulation results show that the introduction of collaboration and auction in proposed method can improve the system revenue.

关键词： multi-service placement deep reinforcement learning auction mechanism

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User Behavior Analysis Based on Big Data and Artificial intelligence 3

User Behavior Analysis Based on Big Data and Artificial Inte...

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3rd international conference on Artificial intelligence, Big Data and Algorithms, CAIBDA 2023

作者： Wang, Tao Ma, Yinghui Wang, Ze Wu, Xiaojun Li, Long Sichuan Zhongdian Qiming Star Information Technology Co. LTD. Sichuan Chengdu610000 China State Grid Qinghai Electric Power Company Information and Communication Company Qinghai Xining810000 China

ISBN: (纸本)9781643684444

Thanks to the support of big data technology, the Internet is gradually expanding towards the Internet of Things, and further upgraded to a better experience of artificial intelligence, which is possible based on big data and artificial intelligence. Taking 'Big data and artificial intelligence can be used for analysis of library users' as an example, Explore the domestic and foreign authoritative database input user behavior analysis and other keywords to find relevant literature, According to the comprehensive comparison of literature analysis method, 55 core technical documents were selected for close reading, Through the development process of technology, Clarify the core technologies and methods of user behavior personality analysis and credibility analysis and do the key expansion research, According to the literature research results combined with the needs of the actual development of the library, Exploring a set of big data analysis model of user behavior based on traditional data mining and classification learning, The model can effectively combine the user personality analysis and abnormal behavior detection to solve the above two problems. © 2023 The Authors.

关键词： Big data

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Facial Landmarks detection system with OpenCV Mediapipe and Python using Optical Flow (Active) Approach 3

Facial Landmarks Detection System with OpenCV Mediapipe and ...

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3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Kumar Rao B, Narendra Panini Challa, Nagendra Krishna, E S Phalguna Chakravarthi, S. Sreenivasa Mohan Babu University School of Computing Tirupati India Amaravati India GITAM School of Technology Dept of CSE Bengaluru India Amrita School of Computing Amrita Vishwa Vidayapeetham Department of Computer Science and Engineering Chennai601103 India

ISBN: (纸本)9798350399264

To achieve positive detection results, a variety of face landmark methods supported by the convolutional neural network have been developed. The instability landmarks thus emerge in video frames as a result of CNNs, on the other hand, are extremely sensitive to input picture noise. This paper provides a light and effective face landmark identification technology based on a lightweight U-Net model based on semantic segmentation and an Optical Flow (Active) Approach (OFA) for solving the problem of landmark shaking. The OFA employs a quick optical flow approach to determine the motion path of the landmark, as well as a route to increasing landmark maintenance. A lightweight U-Net model is used to predict face landmarks with a reduced size of the model and lower computational. To subsume unstable shaking, the predictable face landmarks are given into the OFA technique as well. Finally, various benchmark datasets are used to produce a comparison of many common methodologies as well as the proposed detection process. A lightweight U-Net model is used to model face landmarks in reduced model size and lower computational. To subsume the unstable shaking, the predicted face landmarks are given into the OFA technique as well. Finally, various benchmark datasets are used to produce a comparison of many common methodologies as well as the proposed detection process. © 2023 IEEE.

关键词： Optical flows

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Target detection Based on Improved Yolov4-Tiny Algorithm 3

Target Detection Based on Improved Yolov4-Tiny Algorithm

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3rd international conference on Intelligent Computing and Human-Computer Interaction, ICHCI 2022

作者： Li, HaiFeng Yang, HuiCheng Liang, XinYu Feng, HaiHong School of Electrical Engineering Anhui Polytechnic University Wuhu China Wuhu Angong Cognitive Technology Co LTD China

ISBN: (纸本)9781510661301

At present, the development of artificial intelligence is very rapid, and the intelligent assisted driving system based on deep learning is widely used in the society. For example, in unmanned driving, it can accurately identify pedestrians, vehicles and traffic signs. Convolutional neural network in deep learning has excellent achievements in the field of computer vision and has outstanding feature extraction ability. Therefore, object detection algorithm based on deep learning is a research hotspot in the field of computer vision at present. We propose a vehicle-pedestrian target detection method based on Yolov4-tiny. Firstly, the ResBlock-D module in the ResNet-D network is used to replace one CSPBlock module in Yolov4-tiny, thus reducing the computational complexity. Then, the coordinate attention mechanism is added to help the model better locate and identify targets. Experimental results show that The improved Yolov4-tiny algorithm has higher curacy than the original algorithm, and the Map is improved by 7.8 %, which has a certain reference value for the study of intelligent assisted driving technology. © 2023 SPIE.

关键词： Computer vision

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Tokenization's Potential for Santhali Language Processing 3rd

Tokenization's Potential for Santhali Language Processing

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3rd international conference on Machine Vision and Augmented intelligence, MAI 2023

作者： Ohm, Anand Kumar Panday, Aman Kumar Singh, Koushlendra Kumar Machine Vision and Intelligence Lab Department of CSE National Institute of Technology Jamshedpur Jharkhand India

ISBN: (纸本)9789819743582

Santhali is one of the popular local languages and is mainly spoken by tribal people of States like Jharkhand, Odisha, and West Bengal, as well as a few other parts of the country. Tokenization is one of the major challenges in Natural Language Processing (NLP). Tokenization reduces the length of sentences and paragraphs. There are various methods in use to tokenize different languages depending on the writing and structure of the language. Tokenization is important in many tasks like machine translation, text summarization, chatbot creation, and spam detection. In Natural Language Processing (NLP) most of the Indian spoken languages have high resource availability and have improved technologically in various fields. Still, a low-resource language like SANTHALI does not make much of its existence in technology. The present work is based on the tokenization of Santhali. All available tokenizers to tokenize in the Santhali language have been explored. Ol-Chiki font has been used for the present study. A detailed comparison of tokenization has been represented in the manuscript in the present study. It has been observed that for Santhali a writing system is alphabetic and most of the characters are made of real-world symbols. spaCy model performs better than other available tokenizer models based on the mechanism of the Santhali language. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Natural language processing systems

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Ensemble Kernel-Based Broad Learning system For Fast Gas Recognition in Electronic Nose systems 3

Ensemble Kernel-Based Broad Learning System For Fast Gas Rec...

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3rd international conference on Sensors and Information technology (ICSI)

作者： Li, Wang Zhao, LinJu Wang, Shun Southwest Univ Coll Artificial Intelligence Chongqing Peoples R China

ISBN: (纸本)9781510666122;9781510666139

The rapid recognition of flammable and toxic gases is an essential and challenging task for electronic noses (E-nose) in various fields. The traditional recognition method is limited by the limited features of the gas ultra-time response curve, and has the problems of unsatisfactory classification results and long recognition time. We propose a noval gas recognition algorithm that combines the ensemble learning method with kernel-base learning system learning (KBLS), thereby improving the accuracy of gas recognition, with faster recognition time, and a more stable model. We used four volatile combustible gases as gas test datasets and evaluated the algorithm proposed in this study over a range of response curves from 0.5s to 4s. According to our extensive comparative experiments, it is found that the accuracy is higher than that of other algorithms.

关键词： Electronic nose fast gas recognition kernel-broad learning system (KBLS) ensemble learning method

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