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Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： Mohan K. Warbhe Prateek Verma Rajendra Rewatkar Department of Computer Science and Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Sawangi Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Sawangi Wardha Maharashtra India Department of Biomedical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Sawangi Wardha Maharashtra India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

Robotics and artificial intelligence are becoming a part of the agricultural sector in ways bringing a change in it. It is evident that different technologies can help improve the yields, increase sustainability and efficiency in farming. This paper aims at offering a brief on the use of AI and robotics in agriculture; this will include using technologies such as Exact Hybrid Precision Farming, AI in livestock farming, AI-based resource supply chain applications, and others. In this literature review, the ideas of other authors are compiled and compare the concepts of culture as a factor in tackling the issues affecting agriculture and the possibility of utilizing artificial intelligence in the process. The theoretical literature review of the application of AI and robotics in precision farming aims at presenting literature evidence showcasing the use of activities such as predictive analytics, decision support systems, automated crop monitoring, and robotic harvesting. Other discussed issues are threats including high capital investment during the setting up and implementation of the system, replacement of labor by machines, as well as data insecurities.

关键词： Training Costs Bibliographies Government Supply chains Agriculture artificial intelligence Sustainable development Robots Farming

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Reseek on The Optimization Mould and Equation of E-Commerce Website Composition Based on Markov Mould

Reseek on The Optimization Mould and Equation of E-Commerce ...

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International Conference on Smart Technologies For Smart Nation (SmartTechCon)

作者： Annie S. Sankar Ganesh V. Vidhya Hassan M. Al-Jawahry Suseela D Murali S New Prince Shri Bhavani College Of Engineering And Technology Chennai India Department of Computer Science and Engineering Kommuri Pratap Reddy Institute of Technology Telangana State M.E.T Engineering College Nagercoil India College of Technical Engineering The Islamic University Najaf Iraq Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India

The quality of e-commerce website directly affects the competitiveness of enterprises in the electronic market environment, so how to improve and optimize the website has become a reseek topic that people are very concerned about. The quality of business websites directly affects the competitiveness of enterprises in the electronic market environment, so how to improve and optimize the composition of e-commerce websites has become a question that people are very concerned about. Since the optimization mould has many constraints and traits such as discreteness, it is very difficult to solve the above mould by using the existing graph principle equation. The Markov model, which is a statistical model with a double random path and has seen widespread use in the fields of speech recognition, creaturely sequence analysis, and management, serves as the foundation for this research. The Hidden Markov Model is built on a solid theoretical foundation consisting of probability and statistics, in addition to a robust mathematical framework. Its first applications were in the field of speech recognition and the analysis of creaturely sequences. Over the course of the last few days, individuals have started to investigate and investigate its applicability in the field of management.

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Adaptive Movie Recommendations: A Deep learning Framework with Mood and Sentiment Integration

Adaptive Movie Recommendations: A Deep Learning Framework wi...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： M. Saravana Karthikeyan S. Vijay Shankar R. Santhana Krishnan M. Sornavalli J. Relin Francis Raj P. Sundaravadivel Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Electrical and Electronics Engineering Sona College of Technology Salem India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Mathematics Velammal College of Engineering and Technology Madurai India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Artificial Intelligence and Machine Learning Saveetha Engineering College Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

This research study proposes an advanced movie recommendation system that combines deep learning models with mood detection and sentiment analysis for highly personalized suggestions. The system collects data from diverse sources, including user watch history from OTT platforms along with real-time social media sentiment from social media platforms. Additionally, wearable devices track heart rate to infer the user's emotional state during media interaction. The recommendation engine uses a multi-layered approach. Neural Collaborative Filtering (NCF) analyzes watch patterns, while BERT-based embeddings capture key movie metadata such as genres and actors for content-based filtering. The system also employs an LSTM model for mood-based recommendations, integrating heart rate data and social media sentiment to align suggestions with the user's current mood. Key advantages include a hybrid recommendation approach that combines collaborative filtering, content-based suggestions, and real-time mood analysis. The system adapts to both long-term preferences and short-term emotional states, delivering more relevant and personalized results. Deployed on scalable cloud infrastructure (AWS EC2), with continuous learning from user feedback, the system ensures dynamic and evolving movie recommendations that enhance user satisfaction.

关键词： Heart rate Adaptation models Analytical models Sentiment analysis Accuracy Mood Social networking (online) Collaborative filtering Motion pictures Real-time systems

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Emotion-Cause Relationship Between Clauses Prediction: a Novel Method Based on BERT for Emotion-Cause Pair Extraction

Emotion-Cause Relationship Between Clauses Prediction: a Nov...

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IEEE International Conference on Big Data

作者： Rui Fan Tingting He Yufan Wang Jie Hong Xinhui Tu Faculty of Artificial Intelligence in Education Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning National Language Resources Monitor Research Center for Network Media Central China Normal University Wuhan China School of Computer Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning National Language Resources Monitor Research Center for Network Media School of Computer Central China Normal University Wuhan China National Engineering Research Center for E-Learning Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning National Language Resources Monitor Research Center for Network Media Central China Normal University Wuhan China School of Computer Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning National Language Resources Monitor Research Center for Network Media Central China Normal University Network Media Wuhan China

ISBN: (纸本)9781665480468

Emotion-cause pair extraction (ECPE) aims to obtain all emotion-cause pairs consisting of the emotion clause and the corresponding cause clause in a document. Many existing works for ECPE utilize BERT to obtain representation on each clause in the document, and then perform the classification of Cartesian product among all clause representations or the clause-level sequence tagging. In this paper, we propose to redefine ECPE as the emotion-cause relationship between clauses prediction (ECRP). ECRP fits well with the form of the next sentence prediction task in BERT, which effectively unifies the BERT’s pre-training and the ECPE-specific fi ne-tuning process. According to the task form of ECRP, we reconstruct the original ECPE dataset from the document format to the clause-pair format. The scale of data is effectively expanded, and the imbalance of data is alleviated to a certain extent because some redundant data is filtered out based o n t he r elative distance between clauses. Experiments demonstrate that our ECRP-BERT model outperforms many competitive baselines. Especially in the case of low resources, the ECRP-BERT model still achieves a good performance.

关键词： Analytical models Filtering Bit error rate Big Data Tagging Data models Data mining

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Stochastic Dominant Cognitive Experience Guided Particle Swarm Optimization

Stochastic Dominant Cognitive Experience Guided Particle Swa...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Han-Yang Pan Qiang Yang Ming Li En Zhang Yuan-Yuan Ma Tao Li Dong Liu Jun Zhang College of Computer and Information Engineering Henan Normal University Xinxiang China Key Laboratory of Artificial Intelligence and Personalized Learning in Education of Henan Province Xinxiang China School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing China Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal University Jinhua China Department of Electrical and Electronic Engineering Hanyang University Ansan Korea

This paper proposes a stochastic dominant cognitive experience-guided learning framework for particle swarm optimization (SDCEGPSO) to enhance its search ability in complex environment. Specifically, different from classical PSOs, SDCEGPSO randomly selects dominant cognitive experiences to guide the learning of particles. To this end, the cognitive experiences of all particles, namely their personal best positions, are sorted from the best to the worst. Then, each particle randomly chooses a personal best position better than its own to learn. For the cognitive experience selection, this paper designs three selection methods, namely the random selection, the roulette wheel selection, and the tournament selection. With this learning framework, particles have diverse guiding exemplars to learn from and thus high search diversity is expectedly maintained. Experiments conducted on the 50-D and 100-D CEC2014 problem suite have verified the effectiveness of SDCEGPSO. Compared with the classical global PSO (GPSO) and local PSO (LPSO), SDCEGPSO with the three selection schemes achieve significantly better performance. Besides, among the three selection schemes, the binary tournament selection is the most effective one to help SDCEGPSO solve optimization problems.

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A robust deep ensemble classifier for figurative language detection

arXiv

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arXiv 2021年

作者： Rolandos-Alexandros, Potamias Georgios, Siolas Andreas, Stafylopatis Artificial Intelligence and Learning Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens Greece

Recognition and classification of Figurative Language (FL) is an open problem of Sentiment Analysis in the broader field of Natural Language Processing (NLP) due to the contradictory meaning contained in phrases with metaphorical content. The problem itself contains three interrelated FL recognition tasks: sarcasm, irony and metaphor which, in the present paper, are dealt with advanced Deep learning (DL) techniques. First, we introduce a data prepossessing framework towards efficient data representation formats so that to optimize the respective inputs to the DL models. In addition, special features are extracted in order to characterize the syntactic, expressive, emotional and temper content reected in the respective social media text references. These features aim to capture aspects of the social network user's writing method. Finally, features are fed to a robust, Deep Ensemble Soft Classifier (DESC) which is based on the combinflation of different DL techniques. Using three different benchmark datasets (one of them containing various FL forms) we conclude that the DESC model achieves a very good performance, worthy of comparison with relevant methodologies and state-of-the-art technologies in the challenging field of FL recognition. © 2021, CC BY.

关键词： Sentiment analysis

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Secure Device on boarding in IoT Networks

Secure Device on boarding in IoT Networks

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International Conference on Science Technology engineering and Management (ICONSTEM)

作者： P. Sathyaraj Shankar Nayak Bhukya S. Rukmani Devi Chetan Umadi A. Ajina Rajendiran M Department of Electronics and Communication Engineering RMK College of Engineering and Technology Puduvoyal Tamil Nadu India Department of Computer Science Engineering (Data Science) CMR Technical Campus Hyderabad Telangana India Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai Tamil Nadu India Department of Electronics& Telecommunication Engineering Dayananda Sagar College of Engineering Bengaluru Karnataka India Department of Artificial Intelligence and Machine Learning M S Ramaiah Institute of Technology Bangalore India Department of Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

The fast spread of Internet of Things (IoT) gadgets has led to unprecedented number of interconnected systems offering many applications and services. Diverse elements in IoT networks increase security vulnerabilities, especially when adding new devices. IoT device onboarding secures device integration, authentication, and network connection authorization. This research shows the hazards and limitations of secure IoT device connections. Research examines onboarding protocols' merits and downsides. Bootstrapping, mutual authentication, and key establishment protect the IoT device-network connection in the proposed architecture. Overcoming scalability and resource constraints makes onboarding several IoT devices straightforward. We also analyze IoT network security with PKI and certificate-based authentication. Safe communication pathways and ongoing observation and system adjustments to address new security issues are discussed in this essay. Researchers test the technology in IoT environments. The framework handles onboarding in this case. The method safeguards onboarding and IoT ecosystem data, research demonstrate. A detailed device onboarding approach increases IoT security talks. The framework fixes onboarding issues and vulnerabilities for secure IoT networks.

关键词： Protocols Scalability Ecosystems Authentication Interconnected systems Network security Mathematics

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GSLB: The Graph Structure learning Benchmark

arXiv

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arXiv 2023年

作者： Li, Zhixun Wang, Liang Sun, Xin Luo, Yifan Zhu, Yanqiao Chen, Dingshuo Luo, Yingtao Zhou, Xiangxin Liu, Qiang Wu, Shu Yu, Jeffrey Xu Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong Center for Research on Intelligent Perception Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Department of Automation University of Science and Technology of China China School of Cyberspace Security Beijing University of Posts and Telecommunications China Department of Computer Science University of California Los Angeles United States Heinz College of Information Systems and Public Policy Machine Learning Department School of Computer Science Carnegie Mellon University United States

Graph Structure learning (GSL) has recently garnered considerable attention due to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the computation graph structure simultaneously. Despite the proliferation of GSL methods developed in recent years, there is no standard experimental setting or fair comparison for performance evaluation, which creates a great obstacle to understanding the progress in this field. To fill this gap, we systematically analyze the performance of GSL in different scenarios and develop a comprehensive Graph Structure learning Benchmark (GSLB) curated from 20 diverse graph datasets and 16 distinct GSL algorithms. Specifically, GSLB systematically investigates the characteristics of GSL in terms of three dimensions: effectiveness, robustness, and complexity. We comprehensively evaluate state-of-the-art GSL algorithms in node- and graph-level tasks, and analyze their performance in robust learning and model complexity. Further, to facilitate reproducible research, we have developed an easy-to-use library for training, evaluating, and visualizing different GSL methods. Empirical results of our extensive experiments demonstrate the ability of GSL and reveal its potential benefits on various downstream tasks, offering insights and opportunities for future research. The code of GSLB is available at: https://***/GSL-Benchmark/GSLB. © 2023, CC BY.

关键词： Benchmarking

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A Review on the Applications of Pesticide Spraying Robots in Agricultural Practices

A Review on the Applications of Pesticide Spraying Robots in...

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engineering and Technology (PICET), Parul International Conference on

作者： Mohan K. Warbhe Rajendra M. Rewatkar Prateek Verma Yash S. Pachghare Department of Computer Science and Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Sawangi Wardha Maharashtra India Department of Biomedical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Sawangi Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Sawangi Wardha Maharashtra India

ISBN: (数字)9798350369748

ISBN: (纸本)9798350369755

India is an agriculture based economy and it is one of the most significant sector in India. In traditional agriculture methods, there are various challenges such as reliance on manual labor and limited access to modern farming techniquies due to lack of advanced infrastructure. The spraying of pesticides pose a serious risk to human health as spraying contains potentially hazardous chemicals. This study provides a review on the various techniques being used for spraying pesticides in the agricultural fields. Robots can automate task like planting, weeding and harvesting with better performance in less time. Equipped with sensors and artificial intelligence (AI), they can collect the information to make the accurate decisions about soil, crop, health and weather in real time, thereby reducing costs and environmental impact while enhancing productivity. The robotic spraying provides the advantage over traditional methods as it has the ability to maintain its maximum capacity with best efficiency while the pesticide spraying system evenly cover the plants with spray in the appropriate quantities. This connectivity to smart agriculture systems emphasizes the technology's role as a pillar in the shift to efficient and sustainable farming methods. The deployment of these agricultural robots leads to reduction in risk of the potential hazards linked to human contact with dangerous substances and also supports sustainable agriculture.

关键词： Accuracy Reviews Spraying Crops Pesticides Robot sensing systems Agriculture Robots Water resources Farming

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Real-time Traffic and Traffic Flow Prediction using machine learning Algorithm for Optimal Route Planning in Electric Vehicles

Real-time Traffic and Traffic Flow Prediction using Machine ...

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International Conference on Computational and Characterization Techniques in engineering & Sciences (CCTES)

作者： Satish Sajja Harshit Pandey Aman Sharma Bhawana Pvsn Murty Banajit Rajbongshi Department of Civil Engineering Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada Andhra Pradesh India Department of Mechanical Engineering Graphic Era Hill University India Department of Computer Artificial Intelligence and Machine Learning NIMS University Jaipur Rajasthan India Department of Chemistry Graphic Era Deemed to be University Dehradun India Department of Business Administration Walsh College MI USA Department of Physics Banaras Hindu University Varanasi India

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

Electric vehicles are potential for electrifying traffic due to their effectiveness, environmental friendliness, and cleanliness Not only do electric cars (EVs) provide substantial loads for the electricity grid, but they are also essential to traffic flow. EVs can be used as a flexible resource for grid scheduling. User behaviour affects when EVs travel, and there is a clear bimodal pattern to this. EV usage is highest in the morning and evening, particularly for commuting between homes and places of employment. Their charging and travel patterns affect the road network as well as the electrical grid. In order to address the possible effects of the broad adoption of EVs on the power grid and the aggravation of traffic congestion, the article first builds a dynamic road network strategy. It accomplishes this by utilising road impedance methods, time-varying traffic data, and graph theory. The research's suggested approach can reduce the strain on the local power grid by achieving a more balanced spatiotemporal distribution of charging demand. The 7% safety barrier for power supply voltage loss brought on by an electric vehicle's charging behaviour can be reached using this technique.

关键词： Costs Accuracy Roads Computational modeling Routing Power grids Planning Spatiotemporal phenomena Automobiles Traffic congestion

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