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SA-MSVM:Hybrid Heuristic Algorithm-based Feature Selection for Sentiment Analysis in Twitter

computer Systems science & engineering 2023年第3期44卷 2439-2456页

作者： C.P.Thamil Selvi R.PushpaLaksmi Department of Computer science and Engineering Sri Ranganathar Institute of Engineering and TechnologyCoimbatoreTamilnadu641009India Department of Information and Technology PSNA College of Engineering and TechnologyDindigulTamilnadu624622India

One of the drastically growing and emerging research areas used in most information technology industries is Bigdata *** is created from social websites like Facebook,WhatsApp,Twitter,*** about products,persons,initiatives,political issues,research achievements,and entertainment are discussed on social *** unique data analytics method cannot be applied to various social websites since the data formats are *** approaches,techniques,and tools have been used for big data analytics,opinion mining,or sentiment analysis,but the accuracy is yet to be *** proposed work is motivated to do sentiment analysis on Twitter data for cloth products using Simulated Annealing incorporated with the Multiclass Support Vector Machine(SA-MSVM)***-MSVM is a hybrid heuristic approach for selecting and classifying text-based sentimental words following the Natural Language Processing(NLP)process applied on tweets extracted from the Twitter dataset.A simulated annealing algorithm searches for relevant features and selects and identifies sentimental terms that customers ***-MSVM is implemented,experimented with MATLAB,and the results are *** results concluded that SA-MSVM has more potential in sentiment analysis and classification than the existing Support Vector Machine(SVM)***-MSVM has obtained 96.34%accuracy in classifying the product review compared with the existing systems.

关键词： Bigdata analytics Twitter dataset for cloth product heuristic approaches sentiment analysis feature selection classification

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Transfer Learning Empowered Skin Diseases Detection in Children

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computer Modeling in engineering & sciences 2024年第12期141卷 2609-2623页

作者： Meena N.Alnuaimi Nourah S.Alqahtani Mohammed Gollapalli Atta Rahman Alaa Alahmadi Aghiad Bakry Mustafa Youldash Dania Alkhulaifi Rashad Ahmed Hesham Al-Musallam Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Computer Information Systems College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Computer Engineering College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia ICS Department King Fahd University of Petroleum and MineralsDhahran31261Saudi Arabia

Human beings are often affected by a wide range of skin diseases,which can be attributed to genetic factors and environmental influences,such as exposure to sunshine with ultraviolet(UV)*** left untreated,these diseases can have severe consequences and spread,especially among *** detection is crucial to prevent their spread and improve a patient’s chances of ***,the branch of medicine dealing with skin diseases,faces challenges in accurately diagnosing these conditions due to the difficulty in identifying and distinguishing between different diseases based on their appearance,type of skin,and *** study presents a method for detecting skin diseases using Deep Learning(DL),focusing on the most common diseases affecting children in Saudi Arabia due to the high UV value in most of the year,especially in the *** method utilizes various Convolutional Neural Network(CNN)architectures to classify skin conditions such as eczema,psoriasis,and *** proposed method demonstrates high accuracy rates of 99.99%and 97%using famous and effective transfer learning models MobileNet and DenseNet121,*** illustrates the potential of DL in automating the detection of skin diseases and offers a promising approach for early diagnosis and treatment.

关键词： Deep learning MobileNet DenseNet121 skin diseases detection transfer learning

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Downsampling consistency correction-based quality enhancement for CNN-based light field image super-resolution

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Multimedia Tools and Applications 2024年第34期83卷 80919-80939页

作者： Chung, Kuo-Liang Hsieh, Tsung-Lun Department of Computer Science and Information Engineering National Taiwan University of Science and Technology No. 43 Section 4 Keelung Road Taipei10672 Taiwan

In recent years, numerous CNN-based light field (LF) image super-resolution (SR) methods have been developed. However, due to the downsampling inconsistency between low-resolution (LR) testing LF images and LR training LF images, they may suffer from quality degradation. To address this quality degradation issue, this paper proposes a downsampling consistency correction-based (DCC-based) quality enhancement method. Firstly, a quality-based voting strategy is introduced to identify the downsampling scheme used in the training step. Next, a cascaded Swin Transformer-based recognizer is proposed to identify the downsampled position and downsampling scheme used in the LR testing LF image, after which the proposed DCC-based method is employed to significantly improve the quality of the upsampled LF image. Comprehensive experiments have been conducted on typical LF image datasets to demonstrate the significant quality improvement achieved by our method in comparison to state-of-the-art LF SR methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image enhancement

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Employing Machine Learning Algorithms to Detect Phishing URL Websites 5

Employing Machine Learning Algorithms to Detect Phishing URL...

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

作者： Charishma, T.N.S. Koushik, A Sai Reddy, G Sai Ashmit Himabindu, M. Institute of Aeronautical Engineering Computer Science and Information Technology Hyderabad India

ISBN: (纸本)9798331518097

Phishing attacks have risen like wildfire in the era of new digital technology in the wake of exploiting holes in the electronic communication process while cheating a user to seek private information. This work provides machine learning-based resistance against phishing attacks by differentiating phishing websites using recognition of patterns and analysis on data feature. By training several machine learning models, including Random Forest, XGBoost, and Support Vector Machine (SVM), on a dataset that included characteristics frequently found on phishing websites, we developed a system that could distinguish accurately between genuine websites and phishing attempts. This project brought in a nice innovation through the incorporation of these predictive models into a Flask-developed web application, thus allowing for an interactive, real-time phishing detection. The project shall ensure protection of financial security and personal information by giving users an easy interface to input website links so they can get instant feedback about whether the link is likely to be legitimate. This hybrid technique improves accessibility to non-technical users, brings better detection accuracy, and makes phishing detection more proactive, approachable, and successful in reality. © 2024 IEEE.

关键词： Phishing

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Noise4Denoise:Leveraging noise for unsupervised point cloud denoising

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Computational Visual Media 2024年第4期10卷 659-669页

作者： Weijia Wang Xiao Liu Hailing Zhou Lei Wei Zhigang Deng Manzur Murshed Xuequan Lu Deakin University GeelongVIC 3216Australia Department of Mechanical Engineering and Product Design Engineering Swinburne University of TechnologyMelbourneVIC 3122Australia Department of Computer Science University of HoustonHoustonTX 77204-3027USA Department of Computer Science and Information Technology La Trobe UniversityMelbourneVIC 3086Australia

Existing deep learning-based point cloud denoising methods are generally trained in a supervised manner that requires clean data as ground-truth ***,in practice,it is not always feasible to obtain clean point *** this paper,we introduce a novel unsupervised point cloud denoising method that eliminates the need to use clean point clouds as groundtruth labels during *** demonstrate that it is feasible for neural networks to only take noisy point clouds as input,and learn to approximate and restore their clean *** particular,we generate two noise levels for the original point clouds,requiring the second noise level to be twice the amount of the first noise *** this,we can deduce the relationship between the displacement information that recovers the clean surfaces across the two levels of noise,and thus learn the displacement of each noisy point in order to recover the corresponding clean *** experiments demonstrate that our method achieves outstanding denoising results across various datasets with synthetic and real-world noise,obtaining better performance than previous unsupervised methods and competitive performance to current supervised methods.

关键词： point clouds denoising geometry processing deep learning

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A Study of Blockchain-Based Internet of Things

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer science and Mathematics 2023年第1期4卷 15-23页

作者： Qamar, Roheen Zardari, Baqir Ali Department of Computer Science Quaid-e-Awam University of Engineering Science and Technology Nawabshah Pakistan Department of Information Technology Quaid-e-Awam University of Engineering Science and Technology Nawabshah Pakistan

Blockchain and the Internet of Things (IoT), two of the most emerging technologies, are already reconfiguring our digital future, as described by the drastic change in the current network architecture. The incorporation of IoT has brought the objects around us to life, making them "smart" and capable of communicating with one another, thereby amassing massive data by consistently acquiring the physical world for analysis and intelligent action. It has made our dream of seamless integration of the digital and physical worlds a reality, altering the essence of our perception of the physical world. However, the issue with current IoT solutions is the requirement for a centrally controlled party (similar to a cloud server). This research covers the most essential implications of IoT’s most challenging scenarios, as revealed by the current studies, when connecting and communicating via the Internet. The vast amounts of generated sensitive data pose a significant risk to privacy and security;interestingly, the original architecture design calls for a decentralized system, such as the distributed or peer-to-peer (P2P) system. This scenario highlights the relevance of the blockchain, as it can offer a secure and trustworthy method of sharing information via a distributed/P2P model for achieving transparency, security, privacy, auditability, resilience, access authentication, data immutability, and so on. In this study, we explain how blockchain and IoT technologies are combined to overcome their specific shortcomings and ultimately achieve their maximum benefits. This research also discusses the technical aspects of IoT and bitcoin. We present a comprehensive overview of both technologies, including the blockchain-based IoT (BIoT) architecture and operation, and a few examples of BIoT applications and their comparison. © 2023 Authors. All rights reserved.

关键词： Internet of things

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TSCompiler: efficient compilation framework for dynamic-shape models

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science China(information sciences) 2024年第10期67卷 67-84页

作者： Xiang LUO Chen ZHANG Chenbo GENG Yanzhi YI Jiahui HU Renwei ZHANG Zhen ZHANG Gianpietro CONSOLARO Fan YANG Tun LU Ning GU Li SHANG School of Computer Science Fudan University School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University School of Computer Science and Technology Harbin Institute of Technology Huawei Technologies Co. Ltd. Huawei Paris Research Center School of Microelectronics Fudan University

Today's deep learning models face an increasing demand to handle dynamic shape tensors and computation whose shape information remains unknown at compile time and varies in a nearly infinite range at runtime. This shape dynamism brings tremendous challenges for existing compilation pipelines designed for static models which optimize tensor programs relying on exact shape values. This paper presents TSCompiler, an end-to-end compilation framework for dynamic shape models. TSCompiler first proposes a symbolic shape propagation algorithm to recover symbolic shape information at compile time to enable subsequent optimizations. TSCompiler then partitions the shape-annotated computation graph into multiple subgraphs and fine-tunes the backbone operators from the subgraph within a hardware-aligned search space to find a collection of high-performance schedules. TSCompiler can propagate the explored backbone schedule to other fusion groups within the same subgraph to generate a set of parameterized tensor programs for fused cases based on dependence analysis. At runtime, TSCompiler utilizes an occupancy-targeted cost model to select from pre-compiled tensor programs for varied tensor shapes. Extensive evaluations show that TSCompiler can achieve state-of-the-art speedups for dynamic shape models. For example, we can improve kernel efficiency by up to 3.97× on NVIDIA RTX3090, and 10.30× on NVIDIA A100 and achieve up to five orders of magnitude speedups on end-to-end latency.

关键词： machine learning tensor compilers dynamic shape operator fusion code generation auto-tuning

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MTLS: Making Texts into Linguistic Symbols

MTLS: Making Texts into Linguistic Symbols

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Fei, Wenlong Wang, Xiaohua Hu, Min Zhang, Qingyu Li, Hongbo School of Computer Science and Information Engineering HeFei University of Technology China

ISBN: (纸本)9798891761643

In linguistics, all languages can be considered as symbolic systems, with each language relying on symbolic processes to associate specific symbols with meanings. In the same language, there is a fixed correspondence between linguistic symbol and meaning. In different languages, universal meanings follow varying rules of symbolization in one-to-one correspondence with symbols. Most work overlooks the properties of languages as symbol systems. In this paper, we shift the focus to the symbolic properties and introduce MTLS: a pre-training method to improve the multilingual capability of models by Making Texts into Linguistic Symbols. Initially, we replace the vocabulary in pre-trained language models by mapping relations between linguistic symbols and semantics. Subsequently, universal semantics within the symbolic system serve as bridges, linking symbols from different languages to the embedding space of the model, thereby enabling the model to process linguistic symbols. To evaluate the effectiveness of MTLS, we conducted experiments on multilingual tasks using BERT and RoBERTa, respectively, as the backbone. The results indicate that despite having just over 12,000 pieces of English data in pre-training, the improvement that MTLS brings to multilingual capabilities is remarkably significant. © 2024 Association for Computational Linguistics.

关键词： Semantics

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Improving the review classification of Google apps using combined feature embedding and deep convolutional neural network model

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Journal of Ambient Intelligence and Humanized Computing 2023年第4期14卷 4257-4272页

作者： Aslam, Naila Alzamzami, Ohoud Xia, Kewen Sadiq, Saima Umer, Muhammad Bisogni, Carmen Ashraf, Imran School of Electronics and Information Engineering Hebei University of Technology Tianjin China Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Computer Science Khwaja Fareed University of Engineering and Information Technology Rahim Yar Khan Pakistan Department of Computer Science Information Technology The Islamia University of Bahawalpur Bahawalpur Pakistan Department of Computer Science University of Salerno Fisciano Italy Information and Communication Engineering Yeungnam University Gyeongsan Korea Republic of

Online reviews play an integral part in making mobile applications stand out from the large number of applications available on the Google Play store. Predominantly, users consider posted reviews for appropriate app selection. Manual categorization of such reviews is both inefficient and time-consuming. Therefore, automatic analysis of the sentiments of such reviews provides fast suggestions for new users and facilitates their selection of the appropriate app. However, data imbalance is a major challenge for performing class prediction of such reviews as their distribution is sparse and often leads to low accuracy. This work proposes a framework to overcome this limitation. Extensive experiments are performed using the original and balanced data with the synthetic minority oversampling technique (SMOTE) and adaptive synthetic sampling (ADASYN). Additionally, deep learning and machine learning models are evaluated using FastText, FastText Subword, global vector (GloVe), and their combinations for word representation. Baseline machine learning models, including random forest, extra tree classifier, gradient boosting, Naive Bayes, logistic regression (LR), stochastic gradient descent (SGD), and voting classifier (VC) that combines LR and SGD, are used for comparison. The outcomes show that the convolutional neural network using a combination of word embedding techniques produces the most accurate results. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Convolutional neural networks

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A Novel High-Efficiency Transaction Verification Scheme for Blockchain Systems

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computer Modeling in engineering & sciences 2024年第5期139卷 1613-1633页

作者： Jingyu Zhang Pian Zhou Jin Wang Osama Alfarraj Saurabh Singh Min Zhu School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410015China Science and Technology on Information Systems Engineering Laboratory School of Systems EngineeringNational University of Defense TechnologyChangsha410073China Computer Science Department King Saud UniversityRiyadh11437Saudi Arabia Department of Artificial Intelligence and Big Data Woosong UniversityDaejeon34606South Korea College of Information Science and Technology Zhejiang Shuren UniversityHangzhou310015China

Blockchain can realize the reliable storage of a large amount of data that is chronologically related and verifiable within the *** technology has been widely used and has developed rapidly in big data systems across various *** increasing number of users are participating in application systems that use blockchain as their underlying *** the number of transactions and the capital involved in blockchain grow,ensuring information security becomes *** the verification of transactional information security and privacy has emerged as a critical ***-based verification methods can effectively eliminate the need for centralized third-party ***,the efficiency of nodes in storing and verifying blockchain data faces unprecedented *** address this issue,this paper introduces an efficient verification scheme for transaction ***,it presents a node evaluation module to estimate the activity level of user nodes participating in transactions,accompanied by a probabilistic analysis for all ***,this paper optimizes the conventional transaction organization form,introduces a heterogeneous Merkle tree storage structure,and designs algorithms for constructing these heterogeneous *** analyses and simulation experiments conclusively demonstrate the superior performance of this *** verifying the same number of transactions,the heterogeneous Merkle tree transmits less data and is more efficient than traditional *** findings indicate that the heterogeneous Merkle tree structure is suitable for various blockchain applications,including the Internet of *** scheme can markedly enhance the efficiency of information verification and bolster the security of distributed systems.

关键词： Blockchain architecture transaction verification information security heterogeneous Merkle tree distributed systems

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