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4th International Conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2024

作者： Chang, Lianjie Qu, Fuheng Liu, Chenghao Wang, Enguo Ding, Shiqi Su, Chengzhi Changchun University of Science and Technology School of Computer Science and Technology Changchun China Harbin Aircraft Industry Group Co. Ltd. Workshop 45 Harbin China Changchun University of Science and Technology School of Mechanical and Electrical Engineering Changchun China Changchun University of Science and Technology School of Artificial Intelligence Changchun China

ISBN: (纸本)9798331531225

In the viewpoint planning task for unknown objects, the accuracy of the perception system and calibration lead to the collected point cloud data containing numerous noise and stratification, which interferes with the viewpoint planning *** address the above problems, this paper proposes a method to enhance the robustness of viewpoint planning based on spatial ***, the spatial voxel environment is constructed to store the triangular meshed point cloud containing noise, which is used to record the positional distribution information of the point cloud in space;then, the voxels containing the point cloud are neighborhood-grown to form a Supervoxel unit, which expands the receptive field;And finally, the point cloud boundaries are detected and categorized using spatial Supervoxel, which are used to guide viewpoint *** verify the effectiveness of the proposed method, this paper develops a robot perception system, uses an aircraft radome as the experimental object and introduces varying levels of system errors for the viewpoint planning *** experimental results indicate that the proposed method can accurately detect and classify point cloud boundaries to guide viewpoint planning, achieving complete surface reconstruction of unknown objects, even in the presence of a certain degree of noise in the perception system. The overall modeling time is approximately 7 minutes, demonstrating strong robustness and real-time performance. © 2024 IEEE.

关键词： Robot programming

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CentIER:Accurate centromere identification for plant genomes

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Plant Communications 2024年第10期5卷 12-15页

作者： Dong Xu Jinbao Yang Huaming Wen Wenle Feng Xiaohui Zhang Xingqi Hui Junyang Yue Yun Xu Fei Chen Weihua Pan Shenzhen Branch Guangdong Laboratory of Lingnan Modern AgricultureGenome Analysis Laboratory of the Ministry of Agriculture and Rural AffairsAgricultural Genomics Institute at ShenzhenChinese Academy of Agricultural SciencesShenzhen 518120China Rubber Research Institute Chinese Academy of Tropical Agricultural ScienceHaikouHainan 571101China School of Computer Science and Technology University of Science and Technology of ChinaHefeiAnhui 230026China College of Informatics Huazhong Agricultural UniversityWuhanHubei 430070China School of Horticulture Anhui Agricultural UniversityHefeiAnhui 230026China Nan Fan College Hainan UniversitySanyaHainan 572000China

Dear Editor,Centromeres,the basis for cell division,offer essential insights into cell dynamics,genome stability,and evolutionary processes(McKinley and Cheeseman,2016).Because of ultra-high complexity,high-quality sequences of centromeric regions have long been difficult to obtain,hindering studies of centromere function,evolution,and variation.

关键词： hinder insight stability

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Deep Learning based Frailty Detection in Conjuction with Telemedicine 9

Deep Learning based Frailty Detection in Conjuction with Tel...

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9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Badola, Ayush Chauhan, Rahul Bhatt, Chandradeep Devliyal, Swati Kumar, Gotte Ranjith Pillai, Rudresh Graphic Era Hill University Computer Science And Engineering Uttarakhand Dehradun India Graphic Era Deemed To Be University Computer Science And Engineering Uttarakhand Dehradun India Sr University School of Computer Science & Artificial Intelligence Warangal India Chitkara University Institute of Engineering And Technology Chitkara University Punjab India

ISBN: (纸本)9798350394474

Telemedicine is a service provided by healthcare providers to patients seeking medical attention remotely. This paper explores the role of Artificial Intelligence (AI) and Deep Learning in enhancing the quality of telemedicine services. AI which includes deep learning, data science, and machine learning, enables the development of smart applications that can assist both patients and medical professionals in a more efficient way. Deep Learning is a subset of machine learning. This paper explores the related works, and smart AI models to help physicians to improve telemedicine services. This paper explores the implementation of AI in healthcare services. In this research paper, we propose the implementation of our Heart Attack Risk Predictive model to overcome the challenges in telemedicine services, achieving 98% accuracy. The traditional healthcare process involves multiple steps, scheduling an appointment with the cardiologist undergoing prescribed tests, collecting the test results, and finally receiving a recommendation for the treatment. A theoretical approach of deploying a model within a web portal to eliminate the frictions is discussed in this paper which has the potential to revolutionize remote medical treatment. © 2024 IEEE.

关键词： Telemedicine

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Use of Polygram Substitution in Spotting the Swindler in Secret Sharing 11th

Use of Polygram Substitution in Spotting the Swindler in Sec...

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11th International Conference on Intelligent Computing and Applications, ICRTC 2023

作者： Yadav, Poonam Satheesh Kumar, S. Muthukumaran, V. Sandeep Kumar, Mathivanan Radhika, E.K. Department of Computer Science School of Applied Sciences Reva University Bangalore India Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur 603203 India School of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida203201 India Department of Computer Science and Applications Sindhi College Bangalore560024 India

ISBN: (纸本)9789819717231

In the polygram substitution, one set of characters is replaced by another set based on algorithm. The model is proposed for calculating and estimating secure online secret shares, where dynamic modification in any of the shares will be identified if number of the shares are more than specified threshold value. The security is based on the intractability of factoring as it uses Lagrange polynomial function. This is further enhanced by a graphical masking and unmasking technique;it includes two phases: encryption of the individual secrets and sharing of the data. The generation of the shares is done by masking using Polygram substitution, and the shares are regenerated and unmasking of the qualified shares is done to find original secret. Masking is done on original secret, then shares are generated. Each encrypted share contains information which is different than original secret. Masking by Polygram substitution gives additional protection of secret data on the Internet. Moreover, we achieve efficient recovery of the original secret data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Polynomials

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Multimodal Data Trajectory Prediction: A Review 10

Multimodal Data Trajectory Prediction: A Review

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10th IEEE International Conference on Cyber Security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Wang, Xiaoliang Yue, Hao Yang, Qing Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan China Guangzhou Maritime University School of Information and Communication Engineering Guangzhou China

ISBN: (纸本)9798350312461

Trajectory prediction refers to predicting the future movement of an object, person, or vehicle based on past motion trajectory information and other relevant environmental information. Traditional trajectory prediction solutions are suitable for simple driving scenarios and only applicable for short-term predictions. With the improvement of computing power and data processing speed, people can analyze and utilize large-scale datasets faster, use more data and more complex algorithms to build models, and therefore better predict future trends and behaviors. In addition, different solutions focus on how to efficiently extract features from different types of information and how to more accurately predict the future trajectory of the target object. Our goal is to analyze the impact of different information on prediction, and to classify and compare deep learning-based trajectory prediction methods for vehicles or surrounding pedestrians based on the same data. According to the type and quantity of inputs, they are divided into five categories, and the use and prediction effect of different information solutions are elaborated in detail. © 2023 IEEE.

关键词： Deep neural networks

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Artificial Intelligence Assisted Classifier's and Neural Network Based Prediction and Classification of Parkinson's Disease 4

Artificial Intelligence Assisted Classifier's and Neural Net...

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4th International Conference on Innovative Practices in technology and Management, ICIPTM 2024

作者： Mittal, Khushi Singh Gill, Kanwarpartap Chauhan, Rahul Singh Pokhariya, Hemant Lande, Jayapal Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun India Sr University School of Computer Science & Artificial Intelligence Warangal India

ISBN: (纸本)9798350307757

Parkinson's illness inhibits movement. Early diagnosis is essential for effective treatment. Neural networks and machine learning algorithms are very adept at processing large amounts of data, finding patterns, and producing precise predictions. AI has made medical diagnosis and illness prognosis better, particularly for Parkinson's disease. Artificial intelligence (AI)-driven classifiers and neural network-based prediction systems for Parkinson's disease early detection and staging are two noteworthy applications. As new data is added, these systems can adapt and improve, making them valuable for medical research. Neural network-based Parkinson's disease prediction models have shown promise. These algorithms can analyse patient biomarkers, medical history, and demographics to predict disease development or progression. Thanks to their ability to find complex data connections, neural networks can make personalized predictions and improve early intervention and therapy. AI improves the accuracy, speed of diagnosis and prognosis of Parkinson's disease. Doctors need an accurate diagnosis to quickly initiate the appropriate medications and support programs. Neural network-based prediction models and AI-powered classifiers show promise in treating Parkinson's disease, but AI experts and doctors need to work together. To guarantee the dependability and generalizability of these cutting-edge techniques in clinical practice, data protection, ethical AI usage, and model validation across many patient groups are required. In order to correctly portray Parkinson's illness, the research suggests using five significant classifiers: logistic regression, support vector machines, decision trees, random forests, closest neighbours, and effective sequential neural networks. Among the five classifiers, K-Nearest Neighbour had the highest accuracy rate of 94%, while the sequential neural network predicted an accuracy rate of 90%. © 2024 IEEE.

关键词： Neural network models

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EfficientViT: An Efficient Vision Transformer for Fire and Smoke Image Classification 1

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4th International Conference on Smart Systems: Innovations in Computing, SSIC 2023

作者： Gourisaria, Mahendra Kumar Patel, Ayush V. Chatterjee, Rajdeep Singh, Vijander School of Computer Engineering KIIT Deemed to Be University Odisha Bhubaneswar751024 India Department of Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur302034 India Department of ICT and Natural Science Faculty of Information Technology and Electrical Engineering NTNU—Norwegian University of Science and Technology Aalesund Norway

ISBN: (数字)9789819736904

ISBN: (纸本)9789819736898

Given the variety of fire and smoke, which are distinguished by variances in texture and color, it is extremely difficult to detect fire and smoke from visual imagery. A significant amount of economic and environmental harm has resulted from fire and smoke incidents around the world. In order to prevent property damage and save lives, fires must be detected as soon as possible, for example, forest fires, which frequently occur in the summer yet are easily avoidable. Using cutting-edge deep learning models including Convolutional Neural Network (CNN), VGG16, Vision Transformer (ViT), and MobileViT, we offer a thorough method for classifying fire and smoke in this study. We also provide an innovative model dubbed EfficientViT, which combines the advantages of the MobileViT and Inverted Residual layers. The correct classification of visual images into the categories of fire and smoke is our main goal. We show that EfficientViT beats the other developed models, obtaining an amazing accuracy of 0.92, through a thorough experimentation and evaluation. This demonstrates how well our suggested model works at correctly differentiating between incidents of fire and smoke. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Cyberbullying Detection in Hinglish Language

Cyberbullying Detection in Hinglish Language

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Kumar, Rajiv Rai, Vipin Joshi, Shivani Raj, Daksh Amrita Galgotias University Department of Computer Science & Engineering U.P. Greater Noida India Galgotias College of Engineering and Technology Department of Computer Science and Engineering U.P. Greater Noida India Sharda University Center for Cyber Security and Cryptology Computer Science & Engineering Sharda School of Engineering & Technology Greater Noida India

ISBN: (纸本)9798350378092

Cyberbullying represents one of the most recent challenges stemming from the widespread adoption of social media platforms. With the surge in social media usage, the right to express oneself is being misused. Statistics reveal that approximately 36.7% of individuals believe that they have encountered cyberbullying at some point in their lives. These figures have more than doubled since 2007, and a noticeable increase occurred from 2018 to 2019, indicating an unfavorable trend. While there are existing solutions in the market aimed at mitigating this issue, many of them come with usage limitations or employ fewer effective algorithms. The primary objective of this work is to explore innovative methods for comprehending and automatically identifying instances of cyberbullying across online text messages and comments on different social media platforms. For achieving this goal, real-time data are gathered, including comments, headlines and text messages from trending posts on twitter, and devised a labelling study for categorizing cyberbullying. An analysis is presented of this labelled data, which includes an examination of the relationships between various features and both cyberbullying and cyberaggression. The core focus of this work is the early detection of cyberbullying as it originates, while being drafted in real-time. The aim of this work is to enhance the efficiency of cyberbullying detection by using various machine learning techniques and natural language processing. © 2024 IEEE.

关键词： Phishing

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IoT-Powered Health Monitoring System for Protecting Vital Organs Through Cloud-Based Diagnosis 5th

IoT-Powered Health Monitoring System for Protecting Vital Or...

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5th International Symposium on Signal and Image Processing, ISSIP 2024

作者： Singh, Jagendra Saraswat, Mala Upreti, Kamal Agrawal, Gaurav Hasan, Dler Salih Shaik, Nazeer School of Computer Science Engineering and Technology Bennett University Greater Noida India School of Computer Science Engineering & Technology Bennett University Greater Noida India Delhi-NCR Ghaziabad India KIET Group of Institution Ghaziabad India Department of Computer Science & Information Tech. University of Salahaddin-Erbil Erbil Iraq Department of Computer Science & Engineering Srinivasa Ramanujan Institute of Technology Autonomous Anantapur India

ISBN: (纸本)9789819795147

The main objective of this research was the development and evaluation of an IoT- and machine learning-based health monitoring system capable of protecting patient’s vital organs through cloud diagnosis. This could be achieved by connecting a set of sensors, including temperature, pressure, heart rate, and oxygen sensors, to the patient and allowing them to communicate with the cloud to transmit real-time data via IoT technologies. The data could be further analyzed and predicted using cloud-based machine learning algorithms. This study investigated the performance of different machine learning models, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Naive Bayes (NB), and Negative Decision Trees (DT), for the purpose of patient’s health prediction based on the sensor data. After experimentation and evaluation, we found that the ANN model demonstrated the best predictive ability, with an accuracy level of 99.45%. The SVM, NB, and DT models also demonstrated good performance, with the accuracies of 96.5%, 94.34%, and 91.2%, respectively. Therefore, this research demonstrated that IoT and machine learning technologies could be successfully employed in healthcare for remote patient monitoring and timely prediction. The created system allows for real-time monitoring, which enables early prediction, potentially leading to improved patient outcomes, cost savings, and higher efficiency of provided care. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Remote patient monitoring

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Analysis of Journal Impact Factors Based on Generalized Extreme Value Model 20

Analysis of Journal Impact Factors Based on Generalized Extr...

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20th International computer Conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2023

作者： Yinqi, Luo Jianping, Li Jiaheng, Luo Graduate School University of Electronic Science and Technology of China Chengdu611731 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798350318982

In order to achieve analysis and prediction of journal impact factors, we take Nature magazine as an example in this paper. Firstly, we analyze the rationality of model selection by constructing a Generic Extreme Value model. Next, we calculate the return levels under different return periods and compare them with the data from the test years, to analyze the predictive effect of the Generic Extreme Value model on impact factors. Finally, we analyze the stability of the Generic Extreme Value model in different starting years. The results are shown that: (1) The Generic Extreme Value model and the Gumbel Distribution model are feasible to impact factors. The Generic Extreme Value model does not restrict on the shape parameters and has good flexibility. (2) The return levels and the confidence interval of the return levels can predict the impact factors, and as the return periods increase, the accuracy of the prediction gradually decreases. (3) The Generic Extreme Value model is not sensitive to the starting years, and has good stability. © 2023 IEEE.

关键词： Forecasting

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