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Color image restoration by filtering methods: a review

Soft Computing 2024年第13-14期28卷 7755-7782页

作者： Salamat, Nadeem Missen, Malik Muhammad Saad Akhtar, Nadeem Mustahsan, Muhammad Surya Prasath, V.B. Department of Mathematics Khawaja Fareed University of Engineering and Information Technology Rahim Yar Khan64200 Pakistan Department of Computer Science and IT The Islamia University of Bahawalpur Bahawalpur63100 Pakistan Division of Biomedical Informatics Cincinnati Children’s Hospital Medical Center CincinnatiOH45229 United States Department of Computer Science Pediatrics and Biomedical Informatics University of Cincinnati CincinnatiOH45221 United States

Digital images are corrupted with noise, and image denoising is an important step in image processing modules. In this review, the latest developments in filtering methods for color image restoration are analyzed. These algorithms are compared in terms of objective image quality measures and divided into major classes, such as spatial domain, switching and wavelet filtering methods. These classes are based on the particular methodology used in image denoising algorithms and further subdivided to show their classification in terms of noise models utilized, application style, and stages the filters applied in images. In particular, we present a review of filtering methods in color image denoising, published over the past two decades. Our classification and succinct descriptions of color image restoration by these mathematical filtering techniques and their characterizations can help choose the appropriate ones for various downstream image processing tasks. © The Author(s) 2024.

关键词： Restoration

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Hybridization Model of Frame Semantics and deep learning for text semantic similarity calculation 6

Hybridization Model of Frame Semantics and deep learning for...

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6th International Conference on computer Information Science and Application technology, CISAT 2023

作者： Haijing, Liu Department of Computer Engineering Taiyuan Institute of Technology Taiyuan China College of computing informatics and media University Technology Mara Malaysia

ISBN: (纸本)9781510668546

Text semantic similarity computation is a fundamental problem in the field of natural language processing. In recent years, text semantic similarity algorithms based on deep learning have become the mainstream research paradigm, but they suffer from the problem of insufficient understanding of text semantics and thus unclear interpretation of computational results. In this paper, we propose a text semantic encoding model combining Frame semantic theory and deep learning, which enriches the semantic representation within sentences by making full use of the Frame, Frame Elements, lexical units, and inter-Frame relations in the Frame semantic knowledge base and combining them with the Bert model, and interacts with the semantics between sentences through Siamese Transformer encoders. The experiments are validated on MSRParaphraseCorpus and quora-question-pairs, and the results show that the model proposed in this paper outperforms similar models in terms of F1 values. © 2023 SPIE.

关键词： Semantics

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A Machine Learning-Based Attack Detection and Prevention System in Vehicular Named Data Networking

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computers, Materials & Continua 2023年第11期77卷 1445-1465页

作者： Arif Hussain Magsi Ali Ghulam Saifullah Memon Khalid Javeed Musaed Alhussein Imad Rida State Key Laboratory of Networking and Switching Technology Beijing University of Post and TelecommunicationBeijing100876China Information Technology Center Sindh Agriculture UniversityTandojam70050Pakistan Department of Computer Engineering College of Computing and InformaticsUniversity of SharjahSharjah27272United Arab Emirate Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Laboratory Biomechanics and Bioengineering University of Technology of CompiegneCompiegne60200France

Named Data Networking(NDN)is gaining a significant attention in Vehicular Ad-hoc Networks(VANET)due to its in-network content caching,name-based routing,and mobility-supporting ***,existing NDN faces three significant challenges,including security,privacy,and *** particular,security attacks,such as Content Poisoning Attacks(CPA),can jeopardize legitimate vehicles with malicious *** instance,attacker host vehicles can serve consumers with invalid information,which has dire consequences,including road *** such a situation,trust in the content-providing vehicles brings a new *** the other hand,ensuring privacy and preventing unauthorized access in vehicular(VNDN)is another ***,NDN’s pull-based content retrieval mechanism is inefficient for delivering emergency messages in *** this connection,our contribution is *** existing rule-based reputation evaluation,we propose a Machine Learning(ML)-based reputation evaluation mechanism that identifies CPA attackers and legitimate *** on ML evaluation results,vehicles accept or discard served ***,we exploit a decentralized blockchain system to ensure vehicles’privacy by maintaining their information in a secure digital ***,we improve the default routing mechanism of VNDN from pull to a push-based content dissemination using Publish-Subscribe(Pub-Sub)*** implemented and evaluated our ML-based classification model on a publicly accessible BurST-Asutralian dataset for Misbehavior Detection(BurST-ADMA).We used five(05)hybrid ML classifiers,including Logistic Regression,Decision Tree,K-Nearest Neighbors,Random Forest,and Gaussian Naive *** qualitative results indicate that Random Forest has achieved the highest average accuracy rate of 100%.Our proposed research offers the most accurate solution to detect CPA in VNDN for safe,secure,and reliable vehicle communication.

关键词： Named data networking vehicular networks reputation caching machine-learning

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A Comparison of Multi Class Support Vector Machine vs Deep Convolutional Neural Network for Brain Tumor Classification

A Comparison of Multi Class Support Vector Machine vs Deep C...

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2023 International Seminar on Application for technology of Information and Communication, iSemantic 2023

作者： Cahyo, Nur Ryan Dwi Sari, Christy Atika Rachmawanto, Eko Hari Jatmoko, Cahaya Al-Jawry, Rabei Raad Ali Alkhafaji, Mohamed Ayad University of Dian Nuswantoro Department of Informatics Engineering Semarang Indonesia University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia Northern Technical University Department Computer Science Semarang Indonesia Northern Technical University Department of Computer Engineering Technology Mosul Iraq National University of Science and Technology Department of Computer Sciece DhiQar Iraq

ISBN: (纸本)9798350339215

Multi SVM has long been one of the popular methods in classification, while DCNN has recently gained significant attention in image processing and pattern recognition. This research evaluates the effectiveness of Multi Class Support Vector Machine (M-SVM) and Deep Convolutional Neural Network (DCNN) techniques in classifying brain tumors. A dataset of 2660 3D medical images with dimensions 227 x 227 x 3;including Glioma, Meningioma, and Pituitary tumors, has been partitioned into distinct sets for both training and testing purposes. DCNN approach achieves excellent accuracy in identifying tumor names, with a training accuracy of 97.8% and 100% success rate in 9 experiments. The Multi SVM method demonstrates relatively good accuracy, with training accuracies ranging from 70% to 90% based on different kernel functions. These findings provide valuable insights for selecting appropriate methods in brain tumor classification and encourage further exploration of hybrid Multi SVM-DCNN approaches to enhance accuracy and reliability. © 2023 IEEE.

关键词： Convolutional neural networks

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"I Have to Use My Son's QR Code to Run the Business": Unpacking Senior Street Vendors' Challenges in Mobile Money Collection in China

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Proceedings of the ACM on Human-computer Interaction 2023年第CSCW1期7卷 1-28页

作者： He, Changyang He, Lu Lu, Zhicong Li, Bo Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Hong Kong Department of Informatics University of California Irvine United States Department of Computer Science City University of Hong Kong Hong Kong Hong Kong

Mobile payment systems have become an infrastructural component in citizens' socio-economic life in China. The rapid shift to a cashless society demands vendors of all ages to quickly adapt themselves to the ubiquitous mobile payment era. However, how this trend may impact senior vendors, a group that typically uses less technology, remains unknown despite its significance in inclusive mobile payment design. This work aims to address this gap by investigating the challenges and strategies of senior vendors in mobile payment adoption. Particularly, we focus on a traditional low-resource setting with a large volume of senior vendors: street vending. We conduct a qualitative study incorporating field observations on 33 senior street vendors and semi-structured interviews with 15 of them (aged 53-78), and take Moneywork as an analytical lens to unpack their challenges in physical and social interactions. We find that senior street vendors are a group passively adopting mobile payments due to business requirements instead of recognizing their advantages. Vendors with relatively low digital literacy have to take an alternative method - using family members' QR codes - to run the business as family-dependent money receivers. With limited considerations for senior vendors' situational vulnerabilities, unexpected difficulties of payment confirmation emerge during transactions, such as reduced confirmation efficacy under noisy surroundings and degraded hearing. Transaction security issues also appear when mobile payment-based frauds target both confirmation interfaces (e.g., fake sounds of successful payment) and trust systems (e.g., showing half-done proof to flee without paying) in street vending. Finally, we raise a less visible yet critical concern of family-dependent vendors on the lost money freedom when their income flows into their families' wallets. We propose design implications for mobile payment systems to support more secure, efficient and accessible money collec

关键词： Electronic money

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Sentiment Analysis of Game Reviews on STEAM using BERT, BiLSTM, and CRF

Sentiment Analysis of Game Reviews on STEAM using BERT, BiLS...

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2023 International Conference on Electrical engineering and informatics, ICEEI 2023

作者： Al Mursyidy Fadhlurrahman, Jalaluddin Herawati, Neng Ayu Widya Aulya, Hayyu Rachma Puspasari, Ira Utama, Nugraha Priya School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Dinamika University Computer Engineering Department Surabaya Indonesia

ISBN: (纸本)9798350381290

The Steam platform releases thousands of games almost every week, providing customers with many options, which require various considerations to make a purchase decision. Steam has features that can help consumers consider purchasing games, such as the help dashboard that provides information on game reviews and ratings. However, based on user input reviews, game ratings are still manual, which may cause human error and inaccurate results. To help gamers get the desired game information and increase customer purchasing power towards available games, sentiment analysis is one technique for classifying opinions. This study focuses on sentiment analysis of game reviews, which will result in classification into two classes, positive and negative. It will be measured for its performance based on the f1 score evaluation results. The proposed model that we created is to compare sentiment analysis models using the baseline Bi-directional Long-short Term Memory (BiLSTM), Bidirectional Gated Recurrent Unit (BiGRU), and fine-tuning Bidirectional Encoder Representations from Transformer (BERT). We then propose another model, BiLSTM-CRF, by examining the effect of using the last four hidden states of BERT and comparing it with the last one hidden state of BERT to be used in the BiLSTM-CRF model. Based on the results of sentiment analysis models we developed for the ten most reviewed games on Steam, the BiLSTM model combined with CRF on the last four hidden states yielded the best performance, with an Fl score of 95.2 %. This score exceeds the baseline transformer-based model's Fl score of 88.0%. This is because combining BiLSTM and CRF allows the log-likelihood of the BiLSTM emission sample to be obtained more accurately so the model does not lose its context. © 2023 IEEE.

关键词： Sentiment analysis

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9th International Conference on Remote Sensing and Geoinformation of the Environment, RSCy 2023

作者： Zampas, Ioannis Moses, Mefe Haralambous, Haris Department of Electrical Engineering Computer Engineering and Informatics School of Engineering Zaria810107 Nigeria Department of Geomatics Ahmadu Bello University Zaria810107 Nigeria Frederick University Frederick Research Center Nicosia1036 Cyprus

ISBN: (纸本)9781510668225

The purpose of the present study is to investigate co-seismic earthquake ionospheric signatures as detected by remote sensing instrumentation situated in cyprus by exploiting Total Electron Content measurements from calibrated 1 sec RINEX files from the cyprus Positioning System (CYPOS) GNSS stations and ionograms from the cyprus Digisonde that is monitoring the electron density profile over cyprus at 5 min resolution. Through these observations, we established atmospheric wave activity emanating from the earthquake epicenter that took place in Turkey on the 6 February 2023. The major earthquake shock of 7.8 (Mw) struck southern and central Turkey, as well as northern and western Syria km west-northwest of Gaziantep at 01:17 UTC. Subsequently a second 7.6 (Mw) earthquake occurred at 10:24 UTC to the north-northeast in Kahramanmaras Province. Following the first earthquake no ionospheric signatures were detected but the second earthquake around local noon caused notable acoustic wave activity on the ionosphere. © 2023 SPIE.

关键词： Earthquakes

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Bidirectional LSTM-Based Energy Consumption Forecasting:Advancing AI-Driven Cloud Integration for Cognitive City Energy Management

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computers, Materials & Continua 2025年第5期83卷 2907-2926页

作者： Sheik Mohideen Shah Meganathan Selvamani Mahesh Thyluru Ramakrishna Surbhi Bhatia Khan Shakila Basheer Wajdan Al Malwi Mohammad Tabrez Quasim Department of Computer Science and Engineering Srinivasa Ramanujan CentreSASTRA Deemed UniversityKumbakonam612001India Department of Computer Science and Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bangalore562112India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK University Centre for Research and Development Chandigarh UniversityMohali140413PunjabIndia Centre for Research Impact and Outcome Chitkara University Institute of Engineering and TechnologyChitkara UniversityRajpura140401PunjabIndia Department of Information Systems College ofComputer and Information SciencePrincessNourah bintAbdulrahmanUniversityP.O.Box 84428Riyadh11671Saudi Arabia College of Computer Science Informatics and Computer Systems Department King Khalid UniversityAbha61421Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia

Efficient energy management is a cornerstone of advancing cognitive cities,where AI,IoT,and cloud computing seamlessly integrate to meet escalating global energy *** this context,the ability to forecast electricity consumption with precision is vital,particularly in residential settings where usage patterns are highly variable and *** study presents an innovative approach to energy consumption forecasting using a bidirectional Long Short-Term Memory(LSTM)*** a dataset containing over twomillionmultivariate,time-series observations collected froma single household over nearly four years,ourmodel addresses the limitations of traditional time-series forecasting methods,which often struggle with temporal dependencies and non-linear *** bidirectional LSTM architecture processes data in both forward and backward directions,capturing past and future contexts at each time step,whereas existing unidirectional LSTMs consider only a single temporal *** design,combined with dropout regularization,leads to a 20.6%reduction in RMSE and an 18.8%improvement in MAE over conventional unidirectional LSTMs,demonstrating a substantial enhancement in prediction accuracy and *** to existing models—including SVM,Random Forest,MLP,ANN,and CNN—the proposed model achieves the lowest MAE of 0.0831 and RMSE of 0.2213 during testing,significantly outperforming these *** results highlight the model’s superior ability to navigate the complexities of energy usage patterns,reinforcing its potential application in AI-driven IoT and cloud-enabled energy management systems for cognitive *** integrating advanced machine learning techniqueswith IoT and cloud infrastructure,this research contributes to the development of intelligent,sustainable urban environments.

关键词： Deep learning bidirectional LSTM energy consumption forecasting time-series analysis predictive modeling machine learning in energy management

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Big Five Personality Prediction Based on Indonesian Tweets and Personality Test

Big Five Personality Prediction Based on Indonesian Tweets a...

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2023 International Conference on Electrical engineering and informatics, ICEEI 2023

作者： Parameswara, Made Arbi Mahendra, Farrel Satya Putra Mardika, Muhammad Randy Cahya Puspasari, Ira Utama, Nugraha Priya School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Dinamika University Computer Engineering Department Surabaya Indonesia

ISBN: (纸本)9798350381290

The integration of human resources and artificial intelligence has shown a growing interest in automatically predicting personality traits, particularly in recruitment. To contribute to this field, we propose a new method to assist human resource professionals in Indonesia to identify candidates' personality traits. Biases come from personality tests due to their question format and susceptibility to manipulation on this research, we propose an approach that combines personality tests using the BFI form, which measures OCEAN personality traits, with personality trait predictions derived from Twitter data. This research aims to create a personality prediction model and system that efficiently and effectively screens candidate personality traits in human resources. This research has developed a novel model that combines personality test results with tweets personality prediction with an impressive F1-macro performance of 98%. © 2023 IEEE.

关键词： Machine learning

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A framework for object tracking in videos with complex backgrounds and obstructions

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Neural Computing and Applications 2025年 1-20页

作者： Chang, Tsui-Ping Chen, Tzer-Long Hsiao, Tsung-Chih Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung Taiwan Department of Healthcare Administration and Medical Informatics Kaohsiung Medical University Gaoxiong807 Taiwan Department of Radio Television and Film Shih Hsin University Taipei11604 Taiwan

The research of object tracking in videos utilizes computer vision and machine learning techniques to identify and track objects in the consecutive image frames of videos. The popular algorithms used in the research are YOLO, DeepSORT, StrongSORT, and ByteTrack. YOLO has gained recognition for its hardware efficiency and impressive performance in object detection tasks. It efficiently detects objects in the consecutive image frames of videos. On the other hand, DeepSORT, StrongSORT, and ByteTrack leverage the prediction capabilities to estimate the positions of objects in the next image frames. These algorithms play a significant role to enhance the capabilities of object tracking systems. Recently, many researchers have proposed their methods to enhance the accuracy in object tracking tasks. However, the division of videos into the consecutive image frames for tracking objects can pose challenges, especially when the videos have complex backgrounds and many obstructions in the image frames. In fact, the complex backgrounds with many obstructions in image frames decrease the accuracy of tracking objects in applications. In this study, we present TraObs, a framework for object Tracking in complex backgrounds with Obstructions in the image frames of videos. In TraObs, two novel mechanisms CFM and M-controller are proposed for detectors and trackers to enhance the accuracy of object tracking performance. CFM addresses the tracked objects which are missing in some image frames of videos, while M-controller makes sure the tracked objects with the correct object ids in the system. Some experiments with four testing datasets and benchmark datasets are designed and used respectively to analyze the performance of TraObs. Furthermore, the HOTA and MOTA metrics are used to evaluate our proposed methods. The experimental results also prove the effectiveness of CFM and M-controller in our framework TraObs. © The Author(s), under exclusive licence to Springer-Verlag London Ltd.,

关键词： Image enhancement

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