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AMulti-Layers Information Fused Deep Architecture for Skin Cancer Classification in Smart Healthcare

computers, Materials & Continua 2025年第6期83卷 5299-5321页

作者： Veena Dillshad Muhammad Attique Khan Muhammad Nazir Jawad Ahmad Dina Abdulaziz AlHammadi Taha Houda Hee-Chan Cho Byoungchol Chang Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Artificial Intelligence College of Computer Engineering and SciencePrince Mohammad Bin Fahd UniversityAl-Khobar31952Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Center for Computational Social Science Hanyang UniversitySeoul01000Republic of Korea Department of Computer Science Hanynag UniversitySeoul01000Republic of Korea

Globally,skin cancer is a prevalent form of malignancy,and its early and accurate diagnosis is critical for patient *** evaluation of skin lesions is essential,but several challenges,such as long waiting times and subjective interpretations,make this task *** recent advancement of deep learning in healthcare has shownmuch success in diagnosing and classifying skin cancer and has assisted dermatologists in *** learning improves the speed and precision of skin cancer diagnosis,leading to earlier prediction and *** this work,we proposed a novel deep architecture for skin cancer classification in innovative *** proposed framework performed data augmentation at the first step to resolve the imbalance issue in the selected *** proposed architecture is based on two customized,innovative Convolutional neural network(CNN)models based on small depth and filter *** the first model,four residual blocks are added in a squeezed fashion with a small filter *** the second model,five residual blocks are added with smaller depth and more useful weight information of the lesion *** make models more useful,we selected the hyperparameters through Bayesian Optimization,in which the learning rate is *** training the proposed models,deep features are extracted and fused using a novel information entropy-controlled Euclidean Distance *** final features are passed on to the classifiers,and classification results are ***,the proposed trained model is interpreted through LIME-based localization on the HAM10000 *** experimental process of the proposed architecture is performed on two dermoscopic datasets,HAM10000 and *** obtained an improved accuracy of 90.8%and 99.3%on these datasets,***,the proposed architecture returned 91.6%for the cancer *** conclusion,the proposed architecture accuracy is compared with several pre-trained and state-of-the-art

关键词： Smart health skin cancer internet of things deep learning residual blocks fusion optimization

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Aspect-Based Sentiment Classification of Online Product Reviews Using Hybrid Lexicon-Machine Learning Approach 1

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6th International Conference on Applied Machine Learning and Data Analytics, AMLDA 2023

作者： Asuquo, Daniel Attai, Kingsley Usip, Patience George, Uduak Osang, Francis Department of Computer Science Faculty of Science University of Uyo Uyo Nigeria TETFund Center of Excellence in Computational Intelligence Research UNIUYO Uyo Nigeria Department of Mathematics and Computer Science Ritman University IkotEkpene Ikot Ekpene Nigeria Department of Computer Science National Open University of Nigeria Abuja Nigeria

ISBN: (数字)9783031554865

ISBN: (纸本)9783031554858

Nowadays, a good number of customers express their experience with online products. These reviews have an important role in customers’ purchase decision process. There may be hundreds or thousands of unstructured and heterogeneous reviews for a popular product. Traditional text processing techniques have limited capability in extracting opinions on customers’ product reviews from huge data over the Internet. Although lexical approaches aim to map words to sentiments by building a lexicon, the process of developing a lexicon with sentiment scores for phrases and sentences becomes tedious and time consuming as data volume increases. Currently, text sentiment analysis requires fast and accurate techniques to decode and quantify the emotion in tweets. This paper presents a hybrid framework based on lexicon and machine learning (ML) algorithms to train previously seen tweets in order to predict the sentiments of some new input tweets into positive, negative, and neutral polarities. Tweepy library was used to extract tweets on Laptop reviews to identify some aspects and classify sentiments towards them into specific polarity. After data pre-processing, the implementation in Python used Natural Language Processing package called TextBlob to assign subjectivity and polarity scores to text. The scores were used by the ML algorithms to analyze and classify sentiments. A dataset of 2226 tweets was used for training and testing Support Vector Machine, Random Forest, and Naïve Bayes classifiers. Results indicate that Random Forest classifier outperforms others in the task of classifying sentiments on Laptop reviews with the highest accuracy (96%), precision (97%), and F1-score (96%). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Python

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Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 528-538页

作者： Xiaorui Li Xiaojuan Ban Haoran Qiao Zhaolin Yuan Hong-Ning Dai Chao Yao Yu Guo Mohammad S.Obaidat George Q.Huang the School of Intelligence Science and Technology University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering the Key Laboratory of Intelligent Bionic Unmanned Systems and the Institute of Materials Intelligent Technology Liaoning Academy of Materials IEEE the Department of Computer Science Hong Kong Baptist University the School of Computer and Communication Engineering Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing the School of Computer and Communication Engineering University of Science and Technology Beijing the King Abdullah Ⅱ School of Information Technology The University of Jordan the Department of Computational Intelligence the School of Computing SRM University the School of Engineering The Amity University The Hong Kong Polytechnic University

In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.

关键词： Eddy current testing nondestructive testing semantic segmentation time series analysis

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25th International Conference on Information Integration and Web Intelligence, iiWAS 2023

作者： Yagi, Ryuichi Naoi, Yuma Shiokawa, Hiroaki Department of Computer Science University of Tsukuba Tsukuba Japan Center for Computational Sciences University of Tsukuba Tsukuba Japan

ISBN: (纸本)9783031483158

Numerous fields employ correlation-based searches to provide insights about relationships in a database. Herein we propose a correlated DNA subsequence search for DNA databases to extract DNA subsequences that frequently co-occur with a query. Typical search methods must evaluate the entire database for potential candidates, which is computationally expensive. To overcome this, we propose a threshold-based algorithm to achieve efficient computation. The proposed algorithm extracts all DNA subsequences whose correlation is more significant than the threshold and excludes unlikely candidates. Experiments involving real-world DNA databases show that the proposed method achieves a faster search than baseline algorithms while maintaining a high accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： DNA

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25th International Conference on Information Integration and Web Intelligence, iiWAS 2023

作者： Yasuda, Yuma Shiokawa, Hiroaki Department of Computer Science University of Tsukuba Tsukuba Japan Center for Computational Sciences University of Tsukuba Tsukuba Japan

ISBN: (纸本)9783031483158

Various fields from biomedicine to sports science employ similarity searches in multivariate time series (MvTS) to identify patterns and trends. Given MvTS and a query time series, a similarity search extracts a set of subsequences in the MvTS that yield the most significant similarity to the query. Although this is a fundamental task, similarity searches become computationally expensive as the length of the MvTS increases. Herein an efficient method is proposed to accelerate the search process. Using hash-based indexes for MvTS, our proposed method effectively approximates the similarity of each subsequence pair, enhancing the computational efficiency. An experiment involving synthetic and real-world MvTS datasets demonstrates that our proposed method has a higher efficiency and search accuracy compared to existing approaches. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Time series

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25th International Conference on Information Integration and Web Intelligence, iiWAS 2023

作者： Naoi, Yuma Shiokawa, Hiroaki Department of Computer Science University of Tsukuba Tsukuba Japan Center for Computational Sciences University of Tsukuba Tsukuba Japan

ISBN: (纸本)9783031483158

Graph similarity search (GSS) models chemical compounds as a graph database. GSS is an essential tool for drug discovery because they can find similar graphs (compounds) for a query. Existing GSS methods have two critical limitations. First, handling large databases is time consuming. Second, finding compounds with the structure-activity relationship (SAR), which is vital in drug discovery, remains difficult. Herein a novel graph-based method for chemical compound searches is proposed to overcome these limitations. Since compounds with SAR share similar substructures, the proposed method extracts correlated subgraphs included in a query and explores similar compounds. In practical drug discovery task, our method achieves faster searches and improved accuracy compared to existing methods. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graph Databases

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A Framework of Deep Optimal Features Selection for Apple Leaf Diseases Recognition

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computers, Materials & Continua 2023年第4期75卷 697-714页

作者： Samra Rehman Muhammad Attique Khan Majed Alhaisoni Ammar Armghan Usman Tariq Fayadh Alenezi Ye Jin Kim Byoungchol Chang Department of Computer Science HITEC UniversityTaxilaPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Electrical Engineering College of EngineeringJouf UniversitySakakahSaudi Arabia Department of Management Information Systems CoBAPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

Identifying fruit disease manually is time-consuming, expertrequired,and expensive;thus, a computer-based automated system is widelyrequired. Fruit diseases affect not only the quality but also the *** a result, it is possible to detect the disease early on and cure the fruitsusing computer-based techniques. However, computer-based methods faceseveral challenges, including low contrast, a lack of dataset for training amodel, and inappropriate feature extraction for final classification. In thispaper, we proposed an automated framework for detecting apple fruit leafdiseases usingCNNand a hybrid optimization algorithm. Data augmentationis performed initially to balance the selected apple dataset. After that, twopre-trained deep models are fine-tuning and trained using transfer ***, a fusion technique is proposed named Parallel Correlation Threshold(PCT). The fused feature vector is optimized in the next step using a hybridoptimization algorithm. The selected features are finally classified usingmachine learning algorithms. Four different experiments have been carriedout on the augmented Plant Village dataset and yielded the best accuracy of99.8%. The accuracy of the proposed framework is also compared to that ofseveral neural nets, and it outperforms them all.

关键词： Convolutional neural networks deep learning features fusion features optimization classification

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Human brain computing and brain-inspired intelligence

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National science Review 2024年第5期11卷 8-9页

作者： Jianfeng Feng Viktor Jirsa Wenlian Lu Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence(Fudan University) Ministry of Education Shanghai Center for Mathematical Sciences Fudan University Department of Computer Science University of Warwick Zhangjiang Fudan International Innovation Center Institut de Neurosciences des Systèmes Faculté de Medécine

The relationship between structure, neural activity dynamics and intelligent human brain function is complex and multifaceted with deep roots in several academic disciplines. Its understanding is a fundamental goal of brain research [1] and may indicate the way towards the next generation of artificial intelligence [2]. There are major challenges on the way, several of which are addressed in this Special Topic of National science Review.

关键词： intelligence human brain-inspired computing

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HRNetO:Human Action Recognition Using Unified Deep Features Optimization Framework

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computers, Materials & Continua 2023年第4期75卷 1089-1105页

作者： Tehseen Ahsan Sohail Khalid Shaheryar Najam Muhammad Attique Khan Ye Jin Kim Byoungchol Chang Department of Electrical Engineering Riphah International UniversityPeshawar Rdnear Hajj ComplexI-14IslamabadIslamabad Capital Territory46000Pakistan Departmnt of Computer Science HITEC UniversityTaxila47080Pakistan Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

Human action recognition(HAR)attempts to understand a subject’sbehavior and assign a label to each action *** is more appealingbecause it has a wide range of applications in computer vision,such asvideo surveillance and smart *** attempts have been made in theliterature to develop an effective and robust framework for ***,theprocess remains difficult and may result in reduced accuracy due to severalchallenges,such as similarity among actions,extraction of essential features,and reduction of irrelevant *** this work,we proposed an end-toendframework using deep learning and an improved tree seed optimizationalgorithm for accurate *** proposed design consists of a *** the first step,frame preprocessing is *** the second step,two pre-trained deep learning models are fine-tuned and trained throughdeep transfer learning using preprocessed video *** the next step,deeplearning features of both fine-tuned models are fused using a new ParallelStandard Deviation Padding Max Value *** fused features arefurther optimized using an improved tree seed algorithm,and select the bestfeatures are finally classified by using the machine learning *** was carried out on five publicly available datasets,including UTInteraction,Weizmann,KTH,Hollywood,and IXAMS,and achieved higheraccuracy than previous techniques.

关键词： Action recognition features fusion deep learning features selection

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Traffic Management in Internet of Vehicles Using Improved Ant Colony Optimization

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computers, Materials & Continua 2023年第6期75卷 5379-5393页

作者： Abida Sharif Imran Sharif Muhammad Asim Saleem Muhammad Attique Khan Majed Alhaisoni Marriam Nawaz Abdullah Alqahtani Ye Jin Kim Byoungchol Chang Department of Computer Science COMSATS University IslamabadPakistan Faculty of Computing Riphah International UniversityFaisalabadPakistan Department of Computer Science HITEC UniversityTaxilaPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science UET TaxilaTaxilaPakistan Department of Software Engineering UET TaxilaTaxilaPakistan Software Engineering Department College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityP.O.Box 151Al-Kharj11942KSA Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

The Internet of Vehicles(IoV)is a networking paradigm related to the intercommunication of vehicles using a *** a dynamic network,one of the key challenges in IoV is traffic management under increasing vehicles to avoid ***,optimal path selection to route traffic between the origin and destination is *** research proposed a realistic strategy to reduce traffic management service response time by enabling real-time content distribution in IoV systems using heterogeneous network ***,this work proposed a novel use of the Ant Colony Optimization(ACO)algorithm and formulated the path planning optimization problem as an Integer Linear Program(ILP).This integrates the future estimation metric to predict the future arrivals of the vehicles,searching the optimal *** the mobile nature of IOV,fuzzy logic is used for congestion level estimation along with the ACO to determine the optimal *** model results indicate that the suggested scheme outperforms the existing state-of-the-art methods by identifying the shortest and most cost-effective ***,this work strongly supports its use in applications having stringent Quality of Service(QoS)requirements for the vehicles.

关键词： Internet of vehicles internet of things fuzzy logic optimization path planning

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