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Detection of Toxic Content on Social Networking Platforms Using Fine Tuned ULMFiT Model

Intelligent Automation & Soft Computing 2023年第1期35卷 15-30页

作者： Hafsa Naveed Abid Sohail Jasni Mohamad Zain Noman Saleem Rao Faizan Ali Shahid Anwar Department of Software Engineering Faculty of ScienceUniversity of LahorePakistan Department of Computer Science COMSATS University IslamabadLahore CampusPakistan Institute for Big Data Analyics and Artifcial Intelligence(IBDAAI) Kompleks Al-KhawarizmiUniversii Tekoogi MARA40450Shah AlamSelangorMalaysia TechnoGenics SMC PVT LTD LahorePakistan Department of Computer and Infomation Science Universiti Teknologi PETRONASBandar Ser IskandarTronohPerakMalaysia Depatment of Information Engineering Technology National Skils University IslamabadSector H-8/1Faiz AhmedFaiz RoadIslamabadPakistan

Question and answer websites such as Quora,Stack Overflow,Yahoo Answers and Answer Bag are used by *** users post questions on these websites to get the answers from domain specific *** websites are multilingual meaning they are available in many different *** problem for these types of websites is to handle meaningless and irrelevant *** this paper we have worked on the Quora insincere questions(questions which are based on false assumptions or questions which are trying to make a statement rather than seeking for helpful answers)dataset in order to identify user insincere questions,so that Quora can eliminate those questions from their platform and ultimately improve the communication among users over the ***,a research was carried out with recurrent neural network and pretrained glove word embeddings,that achieved the F1 score of *** proposed study has used a pre-trained ULMFiT *** model has outperformed the previous model with an F1 score of 0.91,which is much higher than the previous studies.

关键词： Machine learning text mining quora mining artificial intelligence natural language processing

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Medical Diagnosis Using Machine Learning:A Statistical Review

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Computers, Materials & Continua 2021年第4期67卷 107-125页

作者： Kaustubh Arun Bhavsar Jimmy Singla Yasser D.Al-Otaibi Oh-Young Song Yousaf Bin Zikria Ali Kashif Bashir Lovely Professional University Jalandhar144411India Department of Information Systems Faculty of Computing and Information Technology in RabighKing Abdulaziz UniversityJeddah21589Saudi Arabia Software Department Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea Department of Computing and Mathematics Manchester Metropolitan UniversityManchesterM156BHUK

Decision making in case of medical diagnosis is a complicated process.A large number of overlapping structures and cases,and distractions,tiredness,and limitations with the human visual system can lead to inappropriate *** learning(ML)methods have been employed to assist clinicians in overcoming these limitations and in making informed and correct decisions in disease *** academic papers involving the use of machine learning for disease diagnosis have been increasingly getting ***,to determine the use of ML to improve the diagnosis in varied medical disciplines,a systematic review is conducted in this *** carry out the review,six different databases are *** and exclusion criteria are employed to limit the ***,the eligible articles are classied depending on publication year,authors,type of articles,research objective,inputs and outputs,problem and research gaps,and ndings and *** the selected articles are analyzed to show the impact of ML methods in improving the disease *** ndings of this study show the most used ML methods and the most common diseases that are focused on by *** also shows the increase in use of machine learning for disease diagnosis over the *** results will help in focusing on those areas which are neglected and also to determine various ways in which ML methods could be employed to achieve desirable results.

关键词： Decision making disease diagnosis machine learning medical disciplines

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Frequency-aware Interaction Network for Ultrasound Image Segmentation

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IEEE Transactions on Circuits and Systems for Video technology 2024年

作者： Wang, Dongfang Zhou, Tao Zhang, Yizhe Gao, Shangbing Yang, Jian Nanjing University Of Science And Technology Pca Lab Key Lab Of Intelligent Percept. And Syst. For High-Dimensional Information Of Ministry Of Education Jiangsu Key Lab Of Image And Video Understanding For Social Security School Of Computer Science And Engineering Nanjing210094 China Huaiyin Institute Of Technology Faculty Of Computer And Software Engineering Huaian223003 China

Accurate segmentation of medical ultrasound images is crucial for guiding treatment decisions and assessing intervention effectiveness. The challenge of segmenting lesions in ultrasound images arises from factors such as low contrast, high speckle noise, artifacts, and blurred boundaries. Furthermore, this complexity varies significantly among lesions in different cases. While methods based on Convolutional Neural Networks (CNNs) and Transformers have shown promising results in this field, each approach possesses distinct advantages and limitations. To address these challenges, we propose a novel Frequency-aware Interaction Network (FINet). At the core of our FINet lies the proposed Multi-scale Frequency-aware Self-attention (MFS) module, which effectively captures multi-scale feature information within the self-attention layer. This enables our network to model both local and global features, capitalizing on the strengths of both CNNs and Transformers. Additionally, a frequency-aware network is introduced to learn the interactions between spatial locations in the frequency domain to enhance detailed feature representation such as edges. Furthermore, we present a collaborative interactive decoder network, in which a Selective Feature Interaction (SFI) module is proposed to facilitate the semantic and boundary feature interaction, resulting in more precise segmentation outcomes. Experimental results on four medical ultrasound image datasets show the superiority of our FINet over other state-of-the-art segmentation methods. More importantly, our model achieves an excellent trade-off between performance and computational efficiency. © 1991-2012 IEEE.

关键词： Ultrasonic imaging

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Two-Layer Smart System to Predict HIV Risk 17

Two-Layer Smart System to Predict HIV Risk

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17th International Conference on Sensing technology, ICST 2024

作者： Nguyen, Trinh Duc Minh Le, Viet Tien Nguyen, Mai Hoang Long Nguyen, Phuc Hau Van Nguyen, Thi Phuoc Hanoi-Amsterdam High School Hanoi Viet Nam Hanoi University of Industry HAUI Faculty of Electronic Engineering Hanoi Viet Nam PT Automotive-AIS FPT Software Ho Chi Minh Viet Nam Electric Power University Faculty of Information Technology Hanoi Viet Nam Thanh Do University Department of Information Technology Hanoi Viet Nam

ISBN: (纸本)9798350374827

In this work, a two-layer privacy protection system was proposed for HIV risk prediction. The system involves various clinic centres, each using its data to build a model to predict HIV risk. The models from each clinic are gathered at a central server/cloud to create a global model. To protect the individual clinic models, they are encrypted before being sent to the server. At the server, the encrypted models are aggregated to create the global model without decryption. The encrypted global model is then distributed to each clinic for use. This method ensures that patient information from each clinic is protected by two layers: modelling and encryption. We used data from Vietnam 2005 to validate the proposed framework. The performance of the proposed method is superior the local model in terms of accuracy and AUC (Area Under The Curve). The enhancement is larger than 10% in comparison with recent works. © 2024 IEEE.

关键词： digital health federated learning HIV prediction homomorphic encryption machine learning

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Multi-Swarm Genetic Gray Wolf Optimizer with Embedded Autoencoders for High-Dimensional Expensive Problems

SSRN

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SSRN 2023年

作者： Zhai, Jiahui Bi, Jing Yuan, Haitao Zhou, Mengchu School of Software Engineering Faculty of Information Technology Beijing University of Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

High-dimensional expensive problems are often encountered in the design and optimization of complex robotic and automated systems and distributed computing systems, and they suffer from a time-consuming fitness evaluation process. It is extremely challenging and difficult to produce promising solutions in high-dimensional search space. This work proposes an evolutionary optimization framework with embedded autoencoders that effectively solve optimization problems with high-dimensional search spaces. Autoencoders provide strong dimension reduction and feature extraction abilities that compress a high-dimensional space to an informative low-dimensional one. Search operations are performed in a low-dimensional space, thereby guiding a whole population to converge to the optimal solution more efficiently. Multiple subpopulations coevolve iteratively in a distributed manner. One subpopulation is embedded by an autoencoder, and the other one is guided by a proposed Multi-swarm Gray wolf optimizer based on Genetic learning (MGG). Thus, the proposed multi-swarm framework is named Autoencoder-based MGG (AMGG). AMGG consists of three proposed strategies that well balance exploration and exploitation abilities, i.e., a Dynamic subgroup Number Strategy for reducing the number of subpopulations, a Subpopulation Reorganization Strategy for sharing useful information about each subpopulation, and a Purposeful Detection Strategy for jumping out of local optima and improving exploration ability. AMGG is compared with several widely used algorithms by using typical benchmark functions and a real-life optimization problem. Extensive experimental results prove that AMGG outperforms its peers in terms of search accuracy and convergence efficiency. © 2023, The Authors. All rights reserved.

关键词： Genetic algorithms

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Aspect-Based Sentiment Analysis on Amharic Text for Evaluating Ethio-Telecom Services

Aspect-Based Sentiment Analysis on Amharic Text for Evaluati...

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Emerging Trends in information technology and engineering (ic-ETITE), Conference on

作者： Tarikwa Tesfa Befikadu Belete Samuel Abera Sudhir Kumar Mohapatra Tapan Kumar Das Department of Software Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia Ethio Telecom Services Addis Ababa Ethiopia Faculty of Emerging Technologies Sri Sri University Cuttack India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

People are increasingly expressing their views and opinions about a company's goods or service on social media these days. For all types of businesses and organizations, sentiment analysis in text can be used to find prospective customers and obtain quick feedback on the services or goods they provided. As a result, the organization has a better chance and opportunity to meet the needs of the consumer. Using sentiment analysis, we may extract important information from the thoughts and emotions of an organization's supporters and clients. It examines natural language content produced about services and goods. Detailed opinion mining at the aspect/feature level to serve consumer and organizational needs. In this study, three different machine learning algorithms, these are Naïve Bayes (NB), Support Vector Machine (SVM), and K- Nearest Neighbor (KNN) are used to build the proposed aspect-based sentiment analysis model from the customer comments in Amharic text which is collected from Ethio-telecom official Facebook page. The collected data is annotated into sentiment polarity and aspect-based sentiment classes by the help of five annotators. After data annotation, different preprocessing tasks were implemented. Bag of words and TF-IDF word representation techniques were used to represent each unique word to their vector representation. The experiment was conducted on those three models and the result was validated. The labeled aspects were not balanced so that a SMOTE class imbalance handling technique was employed to the dataset. Finally, SVM model trained with TF-IDF word representation technique with SMOTE oversampled dataset has achieved the highest accuracy result of 98% as compared to NB and KNN.

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Neurocomputational Modelling of EEG Connectivity: Links Between Depression, Inflammation, and Gut-Microbiome 17th

Neurocomputational Modelling of EEG Connectivity: Links Betw...

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17th International Conference on Brain Informatics, BI 2024

作者： Doborjeh, Zohreh lavin, Daniel Hunter, Kirsty Heym, Nadja Heasman, Bryony Doborjeh, Maryam Kasabov, Nikola Gibson, Glen Sumich, Alexander School of Engineering Computer and Mathematical Sciences Knowledge Engineering and Discovery Research Institute Auckland University of Technology Auckland New Zealand School of Population Health Faculty of Medical and Health Sciences The University of Auckland Auckland New Zealand Department of Sport Science School of Science and Technology Nottingham Trent University Nottingham United Kingdom NTU Psychology School of Social Sciences Nottingham Trent University Nottingham United Kingdom School of Software Engineering Dalian University Dalian China Institute for Information and Communication Technologies Bulgarian Academy of Sciences Sofia Bulgaria Department of Food and Nutritional Sciences The University of Reading Whiteknights Reading United Kingdom Department of Psychology and Neuroscience Auckland University of Technology Auckland New Zealand

ISBN: (纸本)9789819632930

The immune and enteric systems have been implicated in psychopathology, including depressive disorders. However, the precise neurocognitive mechanisms remain unclear. The present study uses Brain-Inspired Spiking Neural Network (SNN) technique to model electroencephalographic (EEG) data as a function of gut-microbiota, inflammation, and depressive symptoms. Forty-two participants (nonclinical cohort) were assessed for Bacteroides-Prevotella (BAC) colonization in faecal samples, Interleukin 6 (IL-6) in peripheral blood, depressive symptoms (self-report of cognitive and non-cognitive symptoms) and brain function (resting state EEG). SNN models were applied to EEG data to generate connection weight values across scalp regions. Connection weights were visualized as a function of 1) depression;2) inflammation;3) gut microbiome. Lower depression and higher BAC were associated with higher frontal connection weights (cognitive depression = bilateral, non-cognitive and BAC = left hemisphere only). Lower cognitive depression and lower IL-6 were associated with higher posterior connection weights. The SNN models achieved higher accuracy for theta and alpha EEG sub-bands, highlighting their relevance in differentiating between groups. These findings emphasize the potential of SNNs in predicting parameters from brain functional connectivity data and developing targeted diagnostic and intervention strategies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electroencephalography

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A-REA: Relation-Aware Graph Convolutional Network Entity Alignment Via a Differential Adjacency Matrix

A-REA: Relation-Aware Graph Convolutional Network Entity Ali...

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Image Processing, Electronics and Computers (IPEC), Asia-Pacific Conference on

作者： Yuemei Dang Chen Qiu Feng Gao Jinguang Gu Lihua Zhang Ghazaleh Arman School of Computer Science and Technology Wuhan University of Science and Technology Wuhan Hubei China Software College East China Jiaotong University Nanchang Jiangxi China Faculty of Information Technology and Computer Engineering Urmia University of Technology Urmia Iran

Entity Alignment is the task of seeking entities with the same semantics from distinct knowledge graphs (KGs). Researchers have recently proposed Graph Convolutional Network (GCN) based approaches to achieve this task. Most existing GCN-based methods embed all the relations into an identical value, making the underlying loss on the diversity of the relationship. Relation semantics of entities and relation structure have benefitted the entity alignment task in the monolingual scenarios. This paper proposes a Relation-Aware Graph Convolutional Network with a Differential Adjacency Matrix (A-REA), which exploits the relation-specific semantics and structural information of entities in the KGs to promote the entity representations. The experiments on DBP-15K and DWY-100K datasets, including three cross-lingual subsets and two cross-knowledge graph sub-datasets, reveal that our approach leads to advance in entity alignment task in both the cross-lingual and cross-knowledge KGs. Discriminating the relation shows the effective signal for entity alignment, especially for the schema heterogeneity of different KGs.

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Quantum Poisson solver without arithmetic

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Intelligent Marine technology and Systems 2024年第1期2卷 1-15页

作者： Wang, Shengbin Wang, Zhimin Cui, Guolong Shi, Shangshang Shang, Ruimin Li, Jiaxin Li, Wendong Wei, Zhiqiang Gu, Yongjian Faculty of Information Science and Engineering Ocean University of China Qingdao China Origin Quantum Computing Company Limited Hefei China Beijing Zhongke Arclight Quantum Software Technology Co. Ltd. Beijing China High Performance Computing Center Pilot National Laboratory for Marine Science and Technology (Qingdao) Qingdao China

Solving differential equations is one of the most promising applications of quantum computing. The Poisson equation has applications in various domains of physics and engineering, including the simulation of ocean current dynamics. Here, we propose an efficient quantum algorithm for solving the one-dimensional Poisson equation based on the controlled Ry rotations. Our quantum Poisson solver (QPS) removes the need for expensive routines such as phase estimation, quantum arithmetic or Hamiltonian simulation. The computational cost of our QPS is 3n in qubits and 5/3n 3 in one- and two-qubit gates, where n is the logarithmic of the number of discrete points. An overwhelming reduction of the constant factors of the big-O complexity is achieved, which is critical to evaluate the practicality of implementing the algorithm on a quantum computer. In terms of the error ε, the complexity is log(1/ε) in qubits and poly(log(1/ε)) in operations. The algorithms are demonstrated using a quantum virtual computing system, and the circuits are executed successfully on the IBM real quantum computers. The present QPS could exhibit a potential real-world application for solving differential equations on noisy intermediate-scale quantum (NISQ) devices.

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Efficient COVID-19 detection using data mining algorithms: a comparison of basic and hybrid approaches

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Soft Computing 2025年第3期29卷 1437-1451页

作者： Saidi, Mohammad Gheibi, Mohammad Ghazikhani, Adel Lotfata, Aynaz Chahkandi, Benyamin Familsamavati, Sajad Behzadian, Kourosh Department of Computer Science Imam Reza International University Mashhad Iran MS of Software Engineering Information and Communication Technology Unit Mashhad Municipality Fire and Safety Services Organization Mashhad Iran Big Data Lab Imam Reza International University Mashhad Iran Institute for Nanomaterials Advanced Technologies and Innovation Technical University of Liberec Liberec46117 Czech Republic Tallinn10615 Estonia Department of Pathology Microbiology and Immunology School of Veterinary Medicine University of California Davis95616 United States Department of Urban and Regional Planning San Jose State University San Jose95192 United States Faculty of Civil and Environmental Engineering Gdansk University of Technology Narutowicza Street 11/12 80-233 Gdansk Poland Tecnologico de Monterrey Escuela de Ingeniería y Ciencias Monterrey Mexico School of Computing and Engineering University of West London London United Kingdom

Accurate and efficient diagnosis of COVID-19 remains a significant challenge due to the limitations of current detection methods, such as blood tests and chest scans, which can be time-consuming and error-prone. This study aims to compare the performance of basic and hybrid data mining algorithms in diagnosing COVID-19, using blood test results and clinical information to identify the most effective approach. A dataset of 200 records from suspected and infected COVID-19 patients, with 23 characteristics and one diagnostic class, was analysed. Nine data mining algorithms were tested: four basic algorithms (Naive Bayes, Support Vector Machine, Decision Tree, K-Nearest Neighbor) and five hybrid algorithms (Random Forest, AdaBoost, Majority Voting, XGBoost, Bagging). The study also integrated Response Surface Methodology (RSM) and Adaptive-Network-based Fuzzy Inference System (ANFIS) to enhance model performance. The Bagging algorithm demonstrated superior performance with an accuracy of 88%, sensitivity of 74%, and F-criterion of 78%. The integration of RSM and ANFIS further showed that a smart model could be developed for efficient pandemic crisis management, achieving up to 100% accuracy when considering key factors like AST, Albumin, and CRP. The findings suggest that Bagging and hybrid data mining algorithms can significantly improve COVID-19 detection, reducing time and errors in identifying exposed individuals. The study highlights the potential of combining machine learning techniques with RSM-ANFIS models for effective pandemic management and decision-making in medical settings. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Support vector machines

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