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Task Offloading Optimization Based on Position Prediction in Pedestrian- Robot Mixed Traffic Flows 8

Task Offloading Optimization Based on Position Prediction in...

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8th IEEE International Conference on computer and Communications, ICCC 2022

作者： Zheng, Dawen Wang, Lusheng Kai, Caihong Peng, Min Key Laboratory of Knowledge Engineering with Big Data Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9781665450515

Service robots play an increasingly important role in people's daily life. The density of pedestrians is large and the movement is irregular in pedestrian-robot mixed traffic flows. Robots are prone to collision with pedestrians, and the tasks to be offloaded are closely related to pedestrians. How to analyze the tasks of robots and select the appropriate roadside unit is an important issue. In this paper, the social force model is used to predict the positions of pedestrians and robots, taking into account the influence of various forces to avoid collisions. A task offloading resource optimization algorithm with position prediction is proposed. According to the predicted information, the size and position distribution of all tasks in the scenario are obtained, and then the neural network trained beforehand based on deep Q-Iearning is used to generate a task offloading strategy. The simulation results show that the running time of the proposed algorithm is very short, and the resource allocation required for task offloading is completed in advance based on the predicted information before robots arriving the corresponding positions. Besides, the algorithm significantly reduces the task offloading delay. © 2022 IEEE.

关键词： Edge computing

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A Classification of Inflammatory Bowel Disease using Ensemble Learning Model 1

A Classification of Inflammatory Bowel Disease using Ensembl...

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1st International Conference on Software, Systems and information technology, SSITCON 2024

作者： Sitaraman, Surendar Rama Adnan, Myasar Mundher Maharajan, K. Krishna Prakash, R. Dhilipkumar, R. Intel Corporation CA United States The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Academy of Research and Education School of Computing Kalasalingam Department of Computer Science and Engineering Krishnankoil India Sona College of Technology Department of Information Technology Salem India New Prince Shri Bhavani College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350352931

Inflammatory Bowel Disease (IBD) is an enduring, intermittent bowel disorder with unidentified mechanism and etiology. It typically contains Ulcerative colitis (UC) and Crohn's disease (CD) that are categorized by several bowel ulcers. An impedance phase displaces dynamic development, accumulating susceptibility to exterior pathogens and theoretically leading to neurotic symptoms which encompasses bowel blockade, intestinal tear and liver lesions, with high disability ratio. The existing models have more overfitting, and class imbalance problems. To overcome that, an Ensemble Learning (EL) model such as Logistic Regression, Random Forest and Gaussian Naive Bayes is proposed in this research. Initially, the collected Gene Expression Series 106817 (GSE106817) dataset is preprocessed with Recurrent MultiArray Average (RMA) Normalization and Log2 Data Transformation to eliminate the noises. Next, Recursive Feature Elimination (RFE) technique is used to reduce high-dimensional data for selecting optimal features;and finally, the proposed EL model gives a precise rate in detecting and classifying IBD as CD, UC and Healthy controls. The proposed EL model gives better results than the other compared existing methods Random Forest-Support Vector Machine (RF-SVM) in the context of recall, accuracy, f1 score, and precision as 99.38%, 99.75%, 94.72%, and 97.94% respectively. © 2024 IEEE.

关键词： Contrastive Learning

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Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurts 41

Long-Tail Learning with Foundation Model: Heavy Fine-Tuning ...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Shi, Jiang-Xin Wei, Tong Zhou, Zhi Shao, Jie-Jing Han, Xin-Yan Li, Yu-Feng National Key Laboratory for Novel Software Technology Nanjing University China School of Artificial Intelligence Nanjing University China School of Computer Science and Engineering Southeast University China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China

The fine-tuning paradigm in addressing long-tail learning tasks has sparked significant interest since the emergence of foundation models. Nonetheless, how fine-tuning impacts performance in long-tail learning was not explicitly quantified. In this paper, we disclose that heavy fine-tuning may even lead to non-negligible performance deterioration on tail classes, and lightweight fine-tuning is more effective. The reason is attributed to inconsistent class conditions caused by heavy fine-tuning. With the observation above, we develop a low-complexity and accurate long-tail learning algorithms LIFT with the goal of facilitating fast prediction and compact models by adaptive lightweight fine-tuning. Experiments clearly verify that both the training time and the learned parameters are significantly reduced with more accurate predictive performance compared with state-of-the-art approaches. The implementation code is available at https://***/shijxcs/LIFT. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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ChatGPT and Sentiment Analysis: Unveiling Emotional Insights in Textual Data 6

ChatGPT and Sentiment Analysis: Unveiling Emotional Insights...

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6th International Conference on Futuristic Trends in Networks and Computing Technologies, FTNCT 2024

作者： Suresh Kumar, N. Khan, Zafar Ali Swaminathan, B. Prakash, S. Jayanthi, S. Kumari, Ragini Karnataka Ramanagara India School of Computer Science and Engineering and Information Science Presidency University Karnataka Bengaluru India Department of DS & AI Faculty of Science and Technology The ICFAI Foundation for Higher Education langana Hyderabad India Department of Computer Science and Engineering Manav Rachna International Institute of Research and Studies Haryana Faridabad India

Sentiment analysis plays a critical role in understanding public opinion on social media platforms. This research article presents an in-depth analysis of sentiment in ChatGPT-generated tweets using Natural Language Processing (NLP) techniques which has implications for various domains, including market research, brand reputation management, and public opinion analysis. The objective is to improve the accuracy and effectiveness of sentiment analysis on ChatGPT-generated content. The study begins by pre-processing the tweet data, including the removal of punctuation, special characters, and user mentions. Tokenization is applied to convert the tweets into a structured format while eliminating stopwords to focus on meaningful words. Stemming and lemmatization techniques are employed to further enhance word normalization. Visualizations, such as word clouds, provide insights into the most frequently used words in the ChatGPT tweets, uncovering prevalent topics and themes within the dataset. Sentiment analysis is conducted using Text-Blob and nltk libraries, comparing nltk's Vader Sentiment Intensity Analyzer outperforms TextBlob, achieving an average accuracy of 85% compared to 76%. A machine learning model is constructed using the LinearSVC algorithm, incorporating the Tfidf Vectorizer for feature extraction. The model achieves an accuracy of 89% in sentiment prediction when trained and evaluated on the tweet dataset. To validate the model's performance, an equalized dataset is created, balancing the number of positive, neutral, and negative tweets. It demonstrates consistent accuracy across different sentiment classes, with average accuracies of 87% for positive sentiment, 92% for neutral sentiment, and 86% for negative sentiment. © 2025 The Author(s). Published by Elsevier B.V.

关键词： Emotion Recognition

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A Novel Approach for Health Analysis Using Machine Learning Approaches 1

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5th International Conference on Smart Technologies in Data Science and Communication, SMART-DSC 2022

作者： Bhattacharya, Debdatta Rao, N. Thirupathi Asish Vardhan, K. Neal Joshua, Eali Stephen Department of Computer Science and Engineering Koneru Laksmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur522302 India Department of Computer Science and Engineering Vignan’s Institute of Information Technology Andhra Pradesh Visakhapatnam530016 India Department of Computer Science and Engineering Bullayya College of Engineering for Women Andhra Pradesh Visakhapatnam India

ISBN: (数字)9789811968808

ISBN: (纸本)9789811968792

Data mining and big data are today the world’s leading technology. These techniques deal with diabetes in the banking sector, health services, cyber-security, voting, insurance, the real state, etc. Diabetes is a constant disease before digestion, and wherever personality and total amount in the body of blood glucose is experienced, the formation of estrogens is also unsatisfactory, otherwise the carcass phones do not react properly to estrogens. The balance in high blood sugar diabetes is notorious for extensive stretch injuries, twitching, difficulty’s evolutionary structure of kidneys, heart, vein, nerves and eyes in particular. That is, the main purpose is to analyze consumption, plan a predictable outcome, using the technique of machine learning and position the classifying model with a medical outcome to the adjacent effect. The system mainly selects the features that make miserable diabetes mellitus in the early detection of extrapolative studies. Different results algorithms display the random forest as well as the decision tree algorithm with the greatest distinguishability of 97.20 and 97.30%. Discreetly, diabetics perform best inspection of information. information. Naive Bayesian has an optimal outcome of precision of 85.43%. Similarly, the study provides a summary of the model highlights selected to develop the data collection precisely. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Big data Data mining Deep learning Diabetes Machine learning Naive Bayesian Random forest SVM

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A Novel Anti-rounding Image Steganography Method for Improved UNet++ 7th

A Novel Anti-rounding Image Steganography Method for Improv...

引用

7th Chinese Conference on Pattern Recognition and computer Vision, PRCV 2024

作者： Duan, Xintao Zhang, Meng Bai, Luwei Xu, Kaiou Bao, Mengru Wu, Yinhang Qin, Chuan College of Computer and Information Engineering Henan Normal University Henan Xinxiang453007 China Key Laboratory Foundation of Artificial Intelligence and Personalized Learning in Education of Henan Province Henan Normal University Henan Xinxiang453007 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

ISBN: (纸本)9789819786916

Deep hiding involves embedding a secret image within a cover image so that the secret becomes imperceptible to ensure the security of steganography. However, in practical applications, accuracy loss is due to data conversion during encoding and decoding. The encoder generates the tensor matrix during training, while the practical application corresponds to the image preservation extraction process;the error caused by the rounding operation in this process will affect the accuracy of the secret information extraction during decoding. Aiming at the currently existing methods ignoring the rounding error problem, this paper proposes a novel anti-rounding image steganography method for improved UNet++. We design an anti-rounding function (ARF) to simulate the real steganographic environment, training the model to overcome the non-differentiable rounding distortion problem. When rounding error is considered, the accuracy of the extracted image decreases. To ensure the image quality in real application scenarios, in this paper, we use the improved UNet++ to ensure the quality of steganographic images by cascading convolution and ECA attention mechanisms and propose the Multiscale Mixing Module (MSMM) for feature fusion. In the extraction stage, the corresponding cover image is selected through a shared cover image library, followed by processing the stego image to obtain a different image before feeding it to the extraction network to guarantee the steganography process’s safety. The experiment results indicate the method’s capability to enhance the imperceptibility of steganography significantly, and the steganography process has better security. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decoding

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Comparative Analysis on Text Detection for Scenic Images using EAST and CTPN 7

Comparative Analysis on Text Detection for Scenic Images usi...

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7th International Conference on Trends in Electronics and Informatics, ICOEI 2023

作者： Chhabra, Prachi Shrivastava, Aparna Gupta, Zatin Jss Academy of Technical Education Department of Information Technology Uttar Pradesh Noida India School of Computing Science & Engineering Galgotias University Department of Computer Science & Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350397284

The stated goal of this study is to conduct a thorough analysis of a text detection model built with deep learning. Text detection and localisation methods can pinpoint the precise location of a picture's text region. The primary goal of text detection is to locate instances of text within images of beautiful landscapes. Text reading in scene images is a rapidly growing field of study due to its numerous practical applications in areas such as image/video interpretation, visual search, automated driving, and blind auxiliary. The primary goal of this study is to compare and contrast two text detectors, the East text detector and the CTPN Text detector, based on various criteria to determine their relative merits and shortcomings. The goal here is to improve the efficiency of detecting landscapes in photographs. Although deep learning is present in both approaches, their capabilities are different. This comparison aims to find a better method for larger datasets to develop a more sophisticated, accurate, and time-efficient detection method. Both techniques have been reviewed and implemented, illuminating the potential for enhanced and precise detection with the EAST detector. The accuracy and loss were measured against a bounding box drawn around the text in the test image, and both models performed satisfactorily. Comparison. The publicly available ICDAR 2015 dataset is used in this study. It features test images (a total of 500) and training images (1,000). © 2023 IEEE.

关键词： Deep learning

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Lunokhod—A Warehouse I-Robot

Lunokhod—A Warehouse I-Robot

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International Conference on Robotics, Control, Automation and Artificial Intelligence, RCAAI 2022

作者： Gogisetty, Karthik Capur, Ashwath Raj Pandey, Ishita Dighe, Praneet Department of Mechatronics Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India Department of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

ISBN: (纸本)9789819946334

Lunokhod is a warehouse intelligent robot conceptualized to carry out various tasks. It will be targeted to use in hazardous environments, thereby helping workers in the workspace to have a healthy atmosphere with striking use of technology to do a given task with minimum human interactions. The set environment is effectively structured by using different types of sensors and building a localized map to navigate the structured environment. Decisions are made on which nodes to navigate and who to assist depending on the real-time data. Tele-op system is set to override in case of any ethical dilemmas. The robot will be able to manoeuvre in any direction while using a standard chassis shape which will make it very easy to move around the warehouse, even in very space-constrained areas. It will be built strong enough to carry the weight of the gripper mounted on it for the pick-and-place operations. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Manipulators

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Discrete Geometric Coded Data Layout for Large-scale Object Storage Systems 21

Discrete Geometric Coded Data Layout for Large-scale Object ...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Tian, Yi Xu, Guangping Yang, Hongzhang Ni, Yue Cao, JiaXin Yang, Lei Tianjin University of Technology School of Computer Science and Engineering Tianjin China Novel Software Technology Tianjin Key Laboratory of Intelligence Computing Tianjin China Key Laboratory of Computer Vision and System Ministry of Education China Information Technology Co. Ltd. China

ISBN: (纸本)9798350329223

Regenerating codes are new network codes proposed to reduce the data required for fault repair, which can improve the recovery efficiency of faulty nodes in data storage systems. However, unlike Reed-Solomon code, which repairs at the granularity of bytes, regenerating codes require data stored in large chunks but leads to severe read amplification, which reads out excess data when degraded read objects and increases degraded read time. That reflects a mutual constraint between improving recovery efficiency and degraded read performance, as manifested in the amplification of data reads, a vital issue considered in this *** solve this problem, we propose a new type of data layout - discrete geometric, which splits the object into a series of geometric sequences of data blocks. They are placed discretely into corresponding containers on the disks at different nodes, with containers of the same size made into a strip for encoding. The discrete characteristic ensures lower repair costs for degraded reads. The geometric characteristic ensures the repair performance of regenerating codes by large blocks, and read amplification can be mitigated through small blocks. To reduce IOPS for discrete geometric, we propose Discrete Geometric-Locally Regenerating Codes (DG-LRCs), guaranteeing lower degraded read latency while improving recovery *** results show that the degraded read time of DG-LRCs compared to regenerating codes combined with geometric partitioning is 22.56% lower at 2Gbpsand 60.56% lower at 4Gbps, and the recovery performance is 7.04 times better than that of RS code. © 2023 IEEE.

关键词： Efficiency

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The Effects of Weather Conditions on Data Transmission in Free Space for Wireless Sensor Networks 3rd

The Effects of Weather Conditions on Data Transmission in Fr...

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3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Kadhim, Aqeel A. Naif, Jolan R. Salman, Muna M. Hoomod, Haider K. Computer Techniques Engineering Department Imam AlKadhim University College Baghdad Iraq Informatics Institute for Postgraduate Studies Iraqi Commission for Computers and Informatics Baghdad Iraq Information Technology Directorate Ministry of Science and Technology Baghdad Iraq College of Education Computer Science Department Al-Mustansiriya University Baghdad Iraq

ISBN: (纸本)9789819708918

It is necessary to collect data measured in open lands, whether for example for monitoring purposes or for border protection. The process of data transmission from active nodes and its next level to the cluster head (CH), followed by follow-up to arrive at the base station (BS), with safety and acceptability, are the main objectives of wireless sensor networks (WSNs). The key benefits of wireless systems are low-cost system installation and loss-free data transfer. Weather conditions in the spacious area affect wireless data transmission that is done between sensor nodes in free space. This study attempted to ascertain the influence of atmospheric and meteorological conditions on the loss of data when utilizing the wireless data transmission systems in a wireless sensor network. For this reason, the volume of data transfer losses that happen in free space because of three types of weather conditions is the main discussion point. The meteorological criteria that can be considered in this research are temperature, clear weather, dust, and rain as the weather elements that affect the Iraqi climate. The correlation analysis's outcomes show that the amount of precipitation and the size of raindrops have a main effect on the losses of data transmission. From a practical point of view, there is no confirmed statistical evidence of the amount of loss in the volume of transmitted data as a result of the amount of rainfall with the use of different transmission frequencies. In this research, we designed an objective routing protocol called Fuzzy Clustering with Ant Colony System FCACS, which has been utilized as a simulation program using the MATLAB 2018b application. The practical results of the proposed protocol for data transmission in free space and under different weather conditions were examined, and the simulation results were compared with those of two well-known routing protocols, which are DEEC and Z-SEP. In terms of the quantity of data received from the CH or BS a

关键词： Data transfer

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