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Hybrid Deep Learning Approach for Automating App Review Classification:Advancing Usability Metrics Classification with an Aspect-Based Sentiment Analysis Framework

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computers, Materials & Continua 2025年第1期82卷 949-976页

作者： Nahed Alsaleh Reem Alnanih Nahed Alowidi Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computer Science and EngineeringUniversity of HailHail81451Saudi Arabia

App reviews are crucial in influencing user decisions and providing essential feedback for developers to improve their *** the analysis of these reviews is vital for efficient review *** traditional machine learning(ML)models rely on basic word-based feature extraction,deep learning(DL)methods,enhanced with advanced word embeddings,have shown superior *** research introduces a novel aspectbased sentiment analysis(ABSA)framework to classify app reviews based on key non-functional requirements,focusing on usability factors:effectiveness,efficiency,and *** propose a hybrid DL model,combining BERT(Bidirectional Encoder Representations from Transformers)with BiLSTM(Bidirectional Long Short-Term Memory)and CNN(Convolutional Neural Networks)layers,to enhance classification *** analysis against state-of-the-art models demonstrates that our BERT-BiLSTM-CNN model achieves exceptional performance,with precision,recall,F1-score,and accuracy of 96%,87%,91%,and 94%,*** contributions of this work include a refined ABSA-based relabeling framework,the development of a highperformance classifier,and the comprehensive relabeling of the Instagram App Reviews *** advancements provide valuable insights for software developers to enhance usability and drive user-centric application development.

关键词： Requirements engineering(RE) app review analysis usabilitymetrics hybrid deep learning BERT-BiLSTM-CNN

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Face Recognition Using LBPH and CNN

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Recent Advances in computer science and Communications 2024年第5期17卷 48-58页

作者： Shukla, Ratnesh Kumar Tiwari, Arvind Kumar Mishra, Ashish Ranjan Department of Computer Science & Engineering Shambhunath Institute of Engineering & Technology Uttar Pradesh Prayagraj211012 India Department of Computer Science & Engineering Kamla Nehru Institute of Engineering & Technology Uttar Pradesh Sultanpur228118 India Department of Computer Science & Engineering Rajkiya Engineering College Uttar Pradesh Sonbhadra231206 India

Objective: The purpose of this paper was to use Machine Learning (ML) techniques to extract facial features from images. Accurate face detection and recognition has long been a problem in computer vision. According to a recent study, Local Binary Pattern (LBP) is a superior facial descriptor for face recognition. A person's face may make their identity, feelings, and ideas more obvious. In the modern world, everyone wants to feel secure from unauthorized authentication. Face detection and recognition help increase security;however, the most difficult challenge is to accurately recognise faces without creating any false identities. Methods: The proposed method uses a Local Binary Pattern Histogram (LBPH) and Convolution Neural Network (CNN) to preprocess face images with equalized histograms. Results: LBPH in the proposed technique is used to extract and join the histogram values into a single vector. The technique has been found to result in a reduction in training loss and an increase in validation accuracy of over 96.5%. Prior algorithms have been reported with lower accuracy when compared to LBPH using CNN. Conclusion: This study demonstrates how studying characteristics produces more precise results, as the number of epochs increases. By comparing facial similarities, the vector has generated the best result. © 2024 Bentham science Publishe.

关键词： Face recognition

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Identification of Human Activity from Video Streaming Smartphone Data Using Intensified VGG16

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International Journal of engineering, Transactions B: Applications 2025年第6期38卷 1340-1352页

作者： Yadav, R.K. Daniel, A. Semwal, V.B. Department of Computer Science and Engineering Amity University Gwalior India Data Science and Engineering Manipal University Jaipur India Department of Computer Science and Engineering Maulana Azad National Institute Technology Bhopal India

Human activity recognition (HAR) techniques pick out and interpret human behaviors and actions by analyzing data gathered from various sensor devices. HAR aims to recognize and automatically categorize human activities using patterns and attributes taken from sensor data. HAR is complex in implementing the algorithm for a self-recorded dataset, including challenges such as age variation, wearing different clothes, environment and surface, the direction of the smartphone camera, and many more. The paper aims to propose a VGG16 deep learning framework including an activation function and different optimizers for classifying human activity from the real-time captured dataset;further, we compare the evaluated results with existing results. The proposed methods achieved 99.88% accuracy with excellent precision, recall, and F_measure values. Comparing the evaluated result with existing outcomes over the WISDM and UCI-HAR datasets. The new things in the article are a self-captured dataset of various aged male, female, and healthy volunteers to perform seven activities. Furthermore, this research uses Tensor Processing Units (TPU) available on Kaggle to improve classification accuracy while reducing error rates and speeding up execution. ©2025 The author(s).

关键词： Smartphones

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Bridging the gap in biomedical information retrieval: Harnessing machine learning for enhanced search results and query semantics

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 9491-9510页

作者： Madhubala, P. Ghanimi, Hayder M.A. Sengan, Sudhakar Abhishek, Kumar Department of Computer Science and Engineering Bharathiyar Institute of Engineering for Women Tamil Nadu Salem India Information Technology Department College of Science University of Warith Al-Anbiyaa Karbala Iraq Computer Science Department College of Computer Science and Information Technology University of Kerbala Karbala Iraq Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli India Department of Mathematics VIT Bhopal Bhopal India

The medical domain faces unique challenges in Information Retrieval (IR) due to the complexity of medical language and terminology discrepancies between user queries and documents. While traditional Keyword-Based Methods (KBM) have limitations, the integration of semantic knowledge bases and concept mapping techniques enhances data organization and retrieval. Addressing the growing demands in the biomedical field, a novel medical Information Retrieval System (IRS) is proposed that employs Deep Learning (DL) and KBM. This system comprises five core steps: pre-processing of texts, document indexing using DL (ELMo) and KBM, advanced query processing, a BiLSTM-based retrieval network for contextual representation, and a KR-R re-ranking algorithm to refine document relevance. The purpose of the system is to give users improved biomedical search results through the integration of all of these techniques into a method that takes into consideration the semantic problems of medical records. An in-depth examination of the TREC-PM track samples from 2017 to 2019 observed an impressive leading MRR score of 0.605 in 2017 and a best-in-class rPrec score of 0.350 in 2019, proving how well able the system is to detect and rank relevant medical records accurately. © 2024 – IOS Press. All rights reserved.

关键词： Information retrieval

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Japanese Sign Language Recognition by Combining Joint Skeleton-Based Handcrafted and Pixel-Based Deep Learning Features with Machine Learning Classification

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computer Modeling in engineering & sciences 2024年第6期139卷 2605-2625页

作者： Jungpil Shin Md.Al Mehedi Hasan Abu Saleh Musa Miah Kota Suzuki Koki Hirooka School of Computer Science and Engineering The University of AizuAizuwakamatsu965-8580Japan Department of Computer Science&Engineering Rajshahi University of Engineering&TechnologyRajshahi6204Bangladesh

Sign language recognition is vital for enhancing communication accessibility among the Deaf and hard-of-hearing *** Japan,approximately 360,000 individualswith hearing and speech disabilities rely on Japanese Sign Language(JSL)for ***,existing JSL recognition systems have faced significant performance limitations due to inherent *** response to these challenges,we present a novel JSL recognition system that employs a strategic fusion approach,combining joint skeleton-based handcrafted features and pixel-based deep learning *** system incorporates two distinct streams:the first stream extracts crucial handcrafted features,emphasizing the capture of hand and body movements within JSL ***,a deep learning-based transfer learning stream captures hierarchical representations of JSL gestures in the second ***,we concatenated the critical information of the first stream and the hierarchy of the second stream features to produce the multiple levels of the fusion features,aiming to create a comprehensive representation of the JSL *** reducing the dimensionality of the feature,a feature selection approach and a kernel-based support vector machine(SVM)were used for the *** assess the effectiveness of our approach,we conducted extensive experiments on our Lab JSL dataset and a publicly available Arabic sign language(ArSL)*** results unequivocally demonstrate that our fusion approach significantly enhances JSL recognition accuracy and robustness compared to individual feature sets or traditional recognition methods.

关键词： Japanese Sign Language(JSL) hand gesture recognition geometric feature distance feature angle feature GoogleNet

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Spatial attention-based hybrid VGG-SVM and VGG-RF frameworks for improved cotton leaf disease detection

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Neural Computing and Applications 2025年第14期37卷 8309-8329页

作者： Pandiyaraju, V. Anusha, B. Senthil Kumar, A.M. Jaspin, K. Venkatraman, Shravan Kannan, A. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Marian Engineering College Trivandram Kerala Trivandrum India Department of Computer Science and Engineering St. Joseph’s Institute of Technology Tamil Nadu Chennai India Department of Information Science and Technology College of Engineering Guindy Anna University Tamil Nadu Chennai India

The agriculture industry's production and food quality have been impacted by plant leaf diseases in recent years. Hence, it is vital to have a system that can automatically identify and diagnose diseases at an initial stage for enhancing the quality of agricultural output and also for stopping the overall plant extinction. Most state-of-the-art methods do not provide appropriate accuracy of identification when the similarity among different diseases is higher and the input leaf images have complex background. In this work, a transfer learning-based automated crop disease recognition system is proposed to handle this problem. The cotton leaf disease detection framework being proposed here includes two key stages: (1) the feature extraction stage, where the convolutional neural network (CNN) model from the visual geometry group (VGG-16) is employed to obtain the deep features from cotton leaf images with complex background. (2) Cotton leaf disease detection stage: Here, the extracted optimal features are input to the machine learning classifiers, namely random forest (RF) and nonlinear support vector machine (SVM), for the initial classification of the diseases. For providing an optimal cotton crop disease detection performance, Kaggle’s four-class cotton disease dataset with complex background is chosen in this work. For the final decision making and analysis, two hybrid models named spatial attention-based hybrid VGG-RF and hybrid VGG-SVM have been proposed in this work for enhancing the decision accuracy. These hybrid models offered an average accuracy of approximately 98.29% and 99.31%, respectively, which is higher than the accuracy provided by the related work. This proved that the proposed hybrid model, namely spatial attention-based VGG-SVM and VGG-RF models, with spatial attention outperformed the state-of-the-art classifiers. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Convolutional neural networks

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Drug-Target Interactions Prediction Based on Signed Heterogeneous Graph Neural Networks

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Chinese Journal of Electronics 2024年第1期33卷 231-244页

作者： Ming CHEN Yajian JIANG Xiujuan LEI Yi PAN Chunyan JI Wei JIANG College of Information Science and Engineering Hunan Normal University School of Computer Science Shaanxi Normal University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Computer Science Department BNU-HKBU United International College

Drug-target interactions(DTIs) prediction plays an important role in the process of drug *** computational methods treat it as a binary prediction problem, determining whether there are connections between drugs and targets while ignoring relational types information. Considering the positive or negative effects of DTIs will facilitate the study on comprehensive mechanisms of multiple drugs on a common target, in this work, we model DTIs on signed heterogeneous networks, through categorizing interaction patterns of DTIs and additionally extracting interactions within drug pairs and target protein pairs. We propose signed heterogeneous graph neural networks(SHGNNs), further put forward an end-to-end framework for signed DTIs prediction, called SHGNN-DTI,which not only adapts to signed bipartite networks, but also could naturally incorporate auxiliary information from drug-drug interactions(DDIs) and protein-protein interactions(PPIs). For the framework, we solve the message passing and aggregation problem on signed DTI networks, and consider different training modes on the whole networks consisting of DTIs, DDIs and PPIs. Experiments are conducted on two datasets extracted from Drug Bank and related databases, under different settings of initial inputs, embedding dimensions and training modes. The prediction results show excellent performance in terms of metric indicators, and the feasibility is further verified by the case study with two drugs on breast cancer.

关键词： Drug-target interactions Signed heterogeneous network Link sign prediction Graph neural networks

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EPASAD:ellipsoid decision boundary based Process-Aware Stealthy Attack Detector

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Cybersecurity 2024年第3期7卷 32-48页

作者： Vikas Maurya Rachit Agarwal Saurabh Kumar Sandeep Shukla Department of Computer Science and Engineering Indian Institute of Technology KanpurkanpurIndia Merkle Science BangaloreIndia

Due to the importance of Critical Infrastructure(Cl)in a nation's economy,they have been lucrative targets for cyber *** critical infrastructures are usually Cyber-Physical Systems such as power grids,water,and sewage treatment facilities,oil and gas pipelines,*** recent times,these systems have suffered from cyber attacks numer-ous *** have been developing cyber security solutions for Cls to avoid lasting *** to standard frameworks,cyber security based on identification,protection,detection,response,and recovery are at the core of these *** of an ongoing attack that escapes standard protection such as firewall,anti-virus,and host/network intrusion detection has gained importance as such attacks eventually affect the physical dynamics of the ***,anomaly detection in physical dynamics proves an effective means to implement *** is one example of anomaly detection in the sensor/actuator data,representing such systems physical *** present EPASAD,which improves the detection technique used in PASAD to detect these micro-stealthy attacks,as our experiments show that PASAD's spherical boundary-based detection fails to *** method EPASAD overcomes this by using Ellipsoid boundaries,thereby tightening the boundaries in various dimen-sions,whereas a spherical boundary treats all dimensions *** validate EPASAD using the dataset produced by the TE-process simulator and the C-town *** results show that EPASAD improves PASAD's average recall by 5.8%and 9.5%for the two datasets,respectively.

关键词： Intrusion detection system Critical infrastructure security Industrial control system Machine learning

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HViTML: Hybrid vision transformer with machine learning-based classification model for glaucomatous eye

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Multimedia Tools and Applications 2025年 1-24页

作者： Singh, Piyush Bhushan Singh, Pawan Dev, Harsh Batra, Devanshu Chaurasia, Brijesh Kumar Department of Information Technology Pranveer Singh Institute of Technology Uttar Pradesh Kanpur India Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Lucknow India Department of Computer Science and Engineering Babu Banarasi Das University Uttar Pradesh Lucknow India Department of Computer Science and Engineering Pranveer Singh Institute of Technology Uttar Pradesh Kanpur India

Glaucoma is currently one of the most significant causes of permanent blindness. Fundus imaging is the most popular glaucoma screening method because of the compromises it has to make in terms of portability, size, and cost. In recent years, convolution neural networks (CNNs) have revolutionized computer vision. Convolution is a "local" CNN technique that is only applicable to a small region surrounding an image. Vision Transformers (ViT) use self-attention, which is a "global" activity since it collects information from the entire image. As a result, the ViT can successfully gather distant semantic relevance from an image. This study examined several optimizers, including Adamax, SGD, RMSprop, Adadelta, Adafactor, Nadam, and Adagrad. With 1750 Healthy and Glaucoma images in the IEEE fundus image dataset and 4800 healthy and glaucoma images in the LAG fundus image dataset, we trained and tested the ViT model on these datasets. Additionally, the datasets underwent image scaling, auto-rotation, and auto-contrast adjustment via adaptive equalization during preprocessing. The results demonstrated that preparing the provided dataset with various optimizers improved accuracy and other performance metrics. Additionally, according to the results, the Nadam Optimizer improved accuracy in the adaptive equalized preprocessing of the IEEE dataset by up to 97.8% and in the adaptive equalized preprocessing of the LAG dataset by up to 92%, both of which were followed by auto rotation and image resizing processes. In addition to integrating our vision transformer model with the shift tokenization model, we also combined ViT with a hybrid model that consisted of six different models, including SVM, Gaussian NB, Bernoulli NB, Decision Tree, KNN, and Random Forest, based on which optimizer was the most successful for each dataset. Empirical results show that the SVM Model worked well and improved accuracy by up to 93% with precision of up to 94% in the adaptive equalization preprocess

关键词： Decision trees

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Isomorphic Classification of First Degree Cellular Automata

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Complex Systems 2025年第1期34卷 59-90页

作者： Vikrant, Vicky Bhattacharjee, Kamalika Mukherjee, Sukanya Department of Computer Science and Engineering National Institute of Technology Tamil Nadu Tiruchirappalli620015 India Department of Computer Science and Engineering Institute of Engineering and Management West Bengal Kolkata700091 India

This paper explores the concept of isomorphism in cellular automata (CAs), focusing on identifying and understanding isomorphic relationships between distinct CAs. A cellular automaton (CA) is said to be isomorphic to another CA if the configuration transition diagrams of the two CAs are identical. To illustrate how isomorphism entails the preservation of structural properties while allowing for variations in state representations, we exhaustively explore the isomorphic reversible rules in the first degree CAs (FDCAs), that is, the simple three-neighborhood CAs represented by eight parameters. The experimental study includes reversible FDCAs for states two to 10 with cell lengths of four to seven under the null boundary condition and states two to seven with cell lengths of four to seven under the periodic boundary condition. Given a CA, we also provide two synthesis methods for identifying the set of CAs that are isomorphic to it. The first method operates by permuting the states of a group of CA cell locations, whereas the second method involves exchanging cells at various points. We demonstrate that, depending on the range of exchanged cell positions, the isomorphic CAs produced in this way may have a bigger neighborhood than the input CA. Finally, we provide an exhaustive classification of the elementary CAs (ECAs) using the first synthesis method and compare it with existing results. © 2025, Complex Systems Publications, Inc. All rights reserved.

关键词： classification cycle structure FDCA first degree cellular automaton parameters reversibility synthesis schemes

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