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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3728-3742页

作者： Tang, Yiming Gao, Jianwei Pedrycz, Witold Hu, Xianghui Xi, Lei Ren, Fuji Hu, Min Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2V4 Canada Hefei University of Technology School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Polish Academy of Sciences Systems Research Institute Warsaw00-901 Poland Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Southeast University School of Computer Science and Engineering Nanjing211189 China University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China

At present, there exist some problems in granular clustering methods, such as lack of nonlinear membership description and global optimization of granular data boundaries. To address these issues, in this study, revolving around the parabolic granular data, we propose an overall architecture for parabolic granular modeling and clustering. To begin with, novel coverage and specificity functions are established, and then a parabolic granular data structure is proposed. The fuzzy c-means (FCM) algorithm is used to obtain the numeric prototypes, and then particle swarm optimization (PSO) is introduced to construct the parabolic granular data from the global perspective under the guidance of principle of justifiable granularity (PJG). Combining the advantages of FCM and PSO, we propose the parabolic granular modeling and optimization (PGMO) method. Moreover, we put forward attribute weights and sample weights as well as a distance measure induced by the Gaussian kernel similarity, and then come up with the algorithm of weighted kernel fuzzy clustering for parabolic granularity (WKFC-PG). In addition, the assessment mechanism of parabolic granular clustering is discussed. In summary, we set up an overall architecture including parabolic granular modeling, clustering, and assessment. Finally, comparative experiments on artificial, UCI, and high-dimensional datasets validate that our overall architecture delivers a good improvement over previous strategies. The parameter analysis and time complexity are also given for WKFC-PG. In contrast with related granular clustering algorithms, it is observed that WKFC-PG performs better than other granular clustering algorithms and has superior stability in handling outliers, especially on high-dimensional datasets. © 2020 IEEE.

关键词： Fuzzy clustering

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Machine Learning-Based Lifespan Prediction Modelling for Electric Submersible Pumps in Oil Wells 2

Machine Learning-Based Lifespan Prediction Modelling for Ele...

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2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Rajakala, J. Gupta, Satyaveer Naidu, Pothala Mutyalu Basi Reddy, A. Revathi, S. Selvan, R. Senthamil Sai Spurthi Institute of Technology Department of Computer Science and Engineering Gangaram India Delhi Technical Campus Department of Electrical Engineering Greater Noida India Vignan Institute of Technology And Science Department of Cse Hyderabad India Mohan Babu University School of Computing Department of Computer Science and Engineering Tirupati India Sri Ramakrishna Institute of Technology Department of Computer Science and Engineering Coimbatore India Annamacharya Institute of Technology and Sciences Department of Electronics and Communication Engineering Tirupati India

ISBN: (纸本)9798350364699

To maintain the output flow rate and prolong the life of oil wells, artificial lift is crucial to the petroleum industry. Electric Submersible Pumps (ESP) are a widely used artificial lift technique due to their ability to generate large flow rates in very deep wells. ESPs have a much shorter lifetime than anticipated, even though they are designed to function in harsh environments including corrosion, high temperatures, and high pressure. Because ESP intervention work is far more expensive than other artificial lift techniques, particularly for offshore wells, ESP failures result in production loss and raise replacement costs. Knowing when an ESP may fail is thus highly helpful in the oil production industry and affects how oil wells are designed, built, and managed. Using three machine learning algorithms - Decision Tree, Random Forest, and Gradient Boosting Machine - this study contributes to the construction of prediction models for ESP lifespan that take into account both dynamic and static ESP features. The model that produced the best results for ESP life cycle prediction was determined by comparing the output of various models. To predict the most influential characteristics on the length of ESP, this research also assessed the influence factor of different operational parameters. By knowing more about ESP behavior, early efforts to avert possible ESP failures may be realized thanks to the study's findings. © 2024 IEEE.

关键词： Submersible pumps

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Weapon Detection Identification and Classification for DCNN and YOLO-V5 Techniques 5

Weapon Detection Identification and Classification for DCNN ...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Ashok Kumar, J. Dhivya, G. Selvaraj, P. Karthikeyan, V. Ganesh Ramesh, S.P. Lakshmi, S. Jeya Saravanakumar, R. Karpagam Academy of Higher Education Department of Computer Applications Tamil Nadu Coimbatore India School of Computer Science and Engineering Department of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida India School of Computing SASTRA Deemed to Be University Thanjavur Tamil Nadu Thirumalaisamudram India Iconix Software Solution Tamil Nadu Tirunelveli India

ISBN: (纸本)9798331518097

Over 850,000 people die every year as a direct result of gun violence, yet civilians hold more than 85% of the world's weapons. Detecting weapons via manual surveillance has not been successful. It is critical to automate a system that detects weapons. A large number of convolution neural network CNN-based automated weapon identification systems have shown promising results. These methods are crucial for weapon identification but expensive. These devices produce a large amount of false negatives since monitoring videos are not high enough quality or visible enough to identify weapons. Finding the sweet spot between fast detection and a few false positives and negatives in weapon detection is the main goal of this research. In addition to defining an Area of Interest, the suggested structure uses YOLO. The models use previously processed images with backgrounds that have been blurred using a median filter. Metrics such as F1 scores, accuracy, recall, False Positive, and False Negative will be used to assess the suggested architecture. This investigation shows that YOLO-v5 can quickly identify and has a high recall rate. The deep convolution neural network DCNN required 0.17 ms for each frame, but this one only required 0.010 ms, which would indicate that security and weapon detection have good promise. Starting with a labeled collection of weapon and non-weapon photos is the initial stage in DCNN and YOLOv5 weapon detection. Use YOLOv5's ability to recognize multiple things within frames to train a real-time object localization and identification model. DCNNs categorize weapons and extract complex data effectively. DCNN classifies objects, whereas YOLOv5 detects, bounds, and tracks them. Optimize both models' hyperparameters and apply data augmentation for better performance. Install the integrated system on cutting-edge equipment for real-time weapon identification and classification. © 2024 IEEE.

关键词： Median filters

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An Interpretable Artificial Intelligence Based Smart Agriculture System

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Computers, Materials & Continua 2022年第8期72卷 3777-3797页

作者： Fariza Sabrina Shaleeza Sohail Farnaz Farid Sayka Jahan Farhad Ahamed Steven Gordon School of Engineering and Technology Central Queensland UniversitySydneyAustralia College of Engineering Science and EnvironmentThe University of NewcastleSydneyAustralia School of Social Sciences Western Sydney UniversitySydneyAustralia Department of Environmental Sciences Macquarie UniversitySydneyAustralia School of Computing Data and MathematicalSciencesWestern Sydney UniversitySydneyAustralia School of Engineering and Technology Central Queensland UniversityCairnsAustralia

With increasing world population the demand of food production has increased *** of Things(IoT)based smart agriculture system can play a vital role in optimising crop yield by managing crop requirements in *** can be an important factor to make such systems trusted and easily adopted by *** this paper,we propose a novel artificial intelligence-based agriculture system that uses IoT data to monitor the environment and alerts farmers to take the required actions for maintaining ideal conditions for crop *** strength of the proposed system is in its interpretability which makes it easy for farmers to understand,trust and use *** use of fuzzy logic makes the system customisable in terms of types/number of sensors,type of crop,and adaptable for any soil types and weather *** proposed system can identify anomalous data due to security breaches or hardware malfunction using machine learning *** ensure the viability of the system we have conducted thorough research related to agricultural factors such as soil type,soil moisture,soil temperature,plant life cycle,irrigation requirement and water application timing for Maize as our target *** experimental results show that our proposed system is interpretable,can detect anomalous data,and triggers actions accurately based on crop requirements.

关键词： Explainable artificial intelligence fuzzy logic internet of things machine learning sensors smart agriculture

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InputJump: Augmented reality-facilitated cross-device input fusion based on spatial and semantic information

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虚拟现实与智能硬件（中英文） 2024年第6期6卷 502-526页

作者： Xin ZENG Xiaoyu WANG Tengxiang ZHANG Yukang YAN Yiqiang CHEN Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 101408China Goertek Inc. Bejing 261031China The Hong Kong University of Science and Technology Hong Kong 999077China Department of Computer Science Hajim School of Engineering and Applied SciencesUniversity of RochesterRochesterNY 14627US

The proliferation of computing devices requires seamless cross-device *** reality(AR)headsets can facilitate interactions with existing computers owing to their user-centered views and natural *** this study,we propose InputJump,a user-centered cross-device input fusion method that maps multi-modal cross-device inputs to interactive elements on graphical *** input jump calculates the spatial coordinates of the input target positions and the interactive elements within the coordinate system of the AR *** also extracts semantic descriptions of inputs and elements using large language models(LLMs).Two types of information from different inputs(e.g.,gaze,gesture,mouse,and keyboard)were fused to map onto an interactive *** proposed method is explained in detail and implemented on both an AR headset and a desktop *** then conducted a user study and extensive simulations to validate our proposed *** results showed that InputJump can accurately associate a fused input with the target interactive element,enabling a more natural and flexible interaction experience.

关键词： Mixed reality Spatial Semantic Large language models

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An Intelligent Security Systems in Cloud computing using Cryptography Techniques 5

An Intelligent Security Systems in Cloud Computing using Cry...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Srilakshmi, V. Chetana, V. Lakshmi Dornala, Raghunadha Reddi Vallabaneni, S. Parvathi Ramanjaiah, Ganji School of Computer Science and Engineering Vit -AP University Andhra Pradesh Amaravati522237 India School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amaravati Campus India Walgreens United States Pvp Siddhartha Institute of Technology Department of It Vijayawada India R v Institute of Technology Dept.of Cse Guntur India

ISBN: (纸本)9798350379945

Delivering computing resources over the internet has seen a paradigm shift towards cloud computing. However, there are serious security risks because cloud environments are distributed and shared. Intelligent Security Systems (ISS) that use cryptographic techniques are now necessary to overcome these issues, such as data confidentiality, integrity, and authenticity;this study developed an ISS for cloud computing, concentrating on cryptographic techniques. The RSA-OAEP (Optimal Asymmetric Encryption Padding) and X.509 algorithms are combined to create the ISS and further cover a variety of cryptographic methods, emphasizing their functions in protecting cloud-based systems, including encryption, digital signatures, and key management. It also looks at improving security systems' threat detection and response capabilities by integrating artificial intelligence (AI) algorithms like Grubbs' Test. In addition, several issues and potential paths for intelligent security systems in cloud computing are examined, highlighting the necessity of ongoing study and development to remain ahead of developing cyber threats. © 2024 IEEE.

关键词： Cloud security

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Enabling an On-demand Access to Community Sentiments using LSTM RNNs Web Service Architecture

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Procedia Computer science 2023年 230卷 584-597页

作者： N Prabakaran A. Anbarasi N. Deepa P Pandiaraja School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamilnadu India 632014 Department of computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai 603203 Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur Chennai 603203 Department of Computer Science and Engineering M. Kumarasamy college of Engineering Karur India 639113

Analyzing community response has always played an important role in marketing and development of various products ranging from services to manufacturing. Analyzing the responses and interpreting them helps improve the quality of the product. This task is traditionally done by community managers using surveys and focus groups. One other hands-off solution would be using the technology to analyze sentiments from users’ digital interactions in various online communities. Sentiment Analysis is the use of Natural Language Processing to process text to identify and retrieve sentiments behind the textual data. Sentiment Analysis can be approached either by supervised classification where a machine learning model is trained using labeled data or lexicon-based unsupervised analysis where a sentiment dictionary is used to find the overall sentiment. The idea is to build a system which presents community sentiment results on discussion threads from reddit on demand with the end user not to concern about data processing. We propose a streamlined pipeline architecture for a web application with several closely connected components in the backend that allows users to simply send the reddit discussion thread's URL, which the backend receives and scrapes the comments using the Reddit API, creates a dataset, cleans it and performs sentiment analysis using a RNNs. We used a Long Short-Term Memory (LSTM) a variant of RNN like Bidirectional RNNs and Multi-layered RNNs are also used to get better results.

关键词： Sentiment Analysis Community Response Analysis Social Media Mining Monitor sentiments Sentiment analysis architecture LSTM RNN On demand architecture

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Design of a New Secured Hierarchical Peer-to-Peer Fog Architecture Based on Linear Diophantine Equation 38th

Design of a New Secured Hierarchical Peer-to-Peer Fog Archit...

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38th International Conference on Computers and Their Applications, CATA 2023

作者： Murad, Saydul Akbar Rahimi, Nick Roy, Indranil Gupta, Bidyut School of Computing Sciences & Computer Engineering University of Southern Mississippi HattiesburgMS United States Department of Computer Science Southeast Missouri State University Cape GirardeauMO United States School of Computing Southern Illinois University CarbondaleIL United States

In recent years, there has been massive growth in the usage of IoT devices. Cloud computing architecture is unable to meet the requirements of bandwidth, real-time response, and latency. To overcome these limitations, fog computing architecture is introduced, which responds to requests from IoT devices and only, if necessary, forwards requests to the cloud. Nonetheless, there are still some requests that need to go to the cloud and get affected by the shortcomings of the cloud. In thi s work, we propose to add a peer-to-peer (P2P) structure to the fog layer. We have considered our recently reported 2-layer non-DHT-based architecture for P2P networks in which at each level of the hierarchy existing networks are all structured and each such network has a diameter of 1 overlay hop. Such low diameters have huge significance in our proposed P2P fog model and improve fog computing by presenting very efficient data lookup algorithms. In this model, fog nodes can work together to complete the client requests. Consequently, fog nodes are able to fulfill the client requests in the fog layer, which ultimately decreased overheads on the cloud. Additionally, to improve the security in communication in the architecture, we have utilized ciphertext policy attribute-based encryption (CP-ABE) and presented a new secure algorithm. © 2023, EasyChair. All rights reserved.

关键词： Fog

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A connectivity index based on adjacent vertices in cubic fuzzy graph with an application

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 11025-11040页

作者： Guan, Hao Sadati, Seyed Hossein Talebi, Ali Asghar Shafi, Jana Khan, Aysha Institute of Computing Science and Technology Guangzhou University Guangzhou China School of Computer Science of Information Technology Qiannan Normal University for Nationalities Duyun China Department of Mathematics University of Mazandaran Babolsar Iran Department of Computer Engineering and Information College of Engineering in Wadi Alddawasir Prince Sattam Bin Abdulaziz University Wadi Alddawasir Saudi Arabia Department of Mathematics Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia

A cubic fuzzy graph is a type of fuzzy graph that simultaneously supports two different fuzzy memberships. The study of connectivity in cubic fuzzy graph is an interesting and challenging topic. This research generalized the neighborhood connectivity index in a cubic fuzzy graph with the aim of investigating the connection status of nodes with respect to adjacent vertices. In this survey, the neighborhood connectivity index was introduced in the form of two numerical and distance values. Some characteristics of the neighborhood connectivity index were investigated in cubic fuzzy cycles, saturated cubic fuzzy cycle, complete cubic fuzzy graph and complementary cubic fuzzy graph. The method of constructing a cubic fuzzy graph with arbitrary neighborhood connectivity index was the other point in this research. The results showed that the neighborhood connectivity index depends on the potential of nodes and the number of neighboring nodes. This research was conducted on the Central Bank's data regarding inter-bank relations and its results were compared in terms of neighborhood connectivity index. © 2024-IOS Press. All rights reserved.

关键词： Graph theory

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Enhanced Artificial Neural Networks for Prostate Cancer Detection and Classification 1

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8th International Conference on Data Management, Analytics and Innovation, ICDMAI 2024

作者： Lingappa, Ediga Jareena Begum, D. Rakesh, Mothe Venkatesh, Bura Kishor, Golla Lakshmi Reddy, L. Telangana State Secunderabad Maisammaguda500100 India Department of Computer Science and Engineering Amrita School of Computing Chennai Amrita Vishwa Vidyapeetham Chennai India Department of Computer Science and Engineering Malla Reddy University Hyderabad India Hyderabad India CSE Department St. Martins Engineering College Secunderabad Dhulapally500100 India

ISBN: (数字)9789819732425

ISBN: (纸本)9789819732418

Histopathological scans identify prostate cancer, which medical professionals say is common in men. Pathologists’ modest differences in prostate cancer grading using the Gleason system persist. This work replicates automated prostate cancer segmentation and classification. The proposed method uses gland-oriented segmentation and classification. Prostate cancer is one of the major killers of men in the USA. Complex masses can cause radiologists to overlook prostate cancer. Recent prostate cancer detection technologies are weak. A robust deep learning ANN and transfer learning are used in this research. Decision Tree, SVM with different kernels, and Bayes are compared. GoogleNet models and machine learning classifiers employ cancer MRI database properties. Morphological, entropy-based, textural, SIFT, and elliptic Fourier descriptors are examples. Performance assessment uses numerous indicators. Specificity, sensitivity, and accuracy transfer learning and ANN (Google Net) yielded the greatest results. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Magnetic resonance imaging

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