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Sentiment Analysis of Low-Resource Language Literature Using Data Processing and Deep Learning

computers, Materials & Continua 2024年第4期79卷 713-733页

作者： Aizaz Ali Maqbool Khan Khalil Khan Rehan Ullah Khan Abdulrahman Aloraini Department of IT and Computer Science Pak-Austria Fachhochschule:Institute of Applied Sciences and TechnologyHaripur22620Pakistan Software Competence Center Hagenberg Softwarepark 32aHagenberg4232Austria Department of Computer Science School of Engineering and Digital SciencesNazarbayev UniversityAstana010000Kazakhstan Department of Information Technology College of ComputerQassim UniversityP.O.Box 1162BuraydahSaudi Arabia

Sentiment analysis, a crucial task in discerning emotional tones within the text, plays a pivotal role in understandingpublic opinion and user sentiment across diverse *** numerous scholars conduct sentiment analysisin widely spoken languages such as English, Chinese, Arabic, Roman Arabic, and more, we come to grapplingwith resource-poor languages like Urdu literature which becomes a challenge. Urdu is a uniquely crafted language,characterized by a script that amalgamates elements from diverse languages, including Arabic, Parsi, Pashtu,Turkish, Punjabi, Saraiki, and more. As Urdu literature, characterized by distinct character sets and linguisticfeatures, presents an additional hurdle due to the lack of accessible datasets, rendering sentiment analysis aformidable undertaking. The limited availability of resources has fueled increased interest among researchers,prompting a deeper exploration into Urdu sentiment analysis. This research is dedicated to Urdu languagesentiment analysis, employing sophisticated deep learning models on an extensive dataset categorized into fivelabels: Positive, Negative, Neutral, Mixed, and Ambiguous. The primary objective is to discern sentiments andemotions within the Urdu language, despite the absence of well-curated datasets. To tackle this challenge, theinitial step involves the creation of a comprehensive Urdu dataset by aggregating data from various sources such asnewspapers, articles, and socialmedia comments. Subsequent to this data collection, a thorough process of cleaningand preprocessing is implemented to ensure the quality of the data. The study leverages two well-known deeplearningmodels, namely Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), for bothtraining and evaluating sentiment analysis performance. Additionally, the study explores hyperparameter tuning tooptimize the models’ efficacy. Evaluation metrics such as precision, recall, and the F1-score are employed to assessthe effectiv

关键词： Urdu sentiment analysis convolutional neural networks recurrent neural network deep learning natural language processing neural networks

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ELA-NIDS: An Improved Ensemble Learning Network Intrusion Detection System for IIoT 21

ELA-NIDS: An Improved Ensemble Learning Network Intrusion De...

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21st IEEE India Council International Conference, INDICON 2024

作者： Saurabh, Kumar Singh, Uphar Vamshi, V. Vyas, Ranjana Vyas, O.P. Indian Institute of Information Technology Department of Information Technology Allahabad India Bennett University School of Computer Science Engineering and Technology Greater Noida India

ISBN: (纸本)9798350391282

The Industrial Internet of Things (IIoT) has revolutionised the industrial sector by establishing connectivity between machines and devices through the internet. The heightened level of connectivity has concomitantly amplified the vulnerability to cyber-attacks, which pose a threat to the uninterrupted functioning of organisations leading to economic and reputational damages for businesses, as well as the misappropriation of confidential data. Machine Learning(ML) based Network Intrusion Detection Systems(NIDS) have gained notable popularity to safeguard against security breaches by monitoring anomalous behaviours. The feature sets of the presently accessible NIDS datasets exhibit significant differences which significantly influences the performance of ML models making them unreliable. The proposed approach is based on the NetFlow-based (NF-ToN-IoT-v2) dataset, which is a standardised feature set for NIDS datasets. The primary contribution of this study lies in its capacity to systematically and dependably assess the effectiveness of Ensemble and ML-based traffic classifiers across a diverse array of network topologies, attacks, and other relevant factors. The experimental findings indicate that XG-Boost exhibited a maximum accuracy of 96.01 % in detecting various forms of attacks in an IIoT network. © 2024 IEEE.

关键词： Attack Detection Cyber security IIoT Intrusion Detection System

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Defaulter detection based on Spearman correlation with hyper tuned SVM classification for preventing non-performing assets occurring in bank

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Multimedia Tools and Applications 2024年 1-23页

作者： Mallik, Rajib Puri, Shalini Bhansali, Ashok Manikandan, R. Rahunathan, L. Agartala799046 India Department of Information Technology Manipal University Jaipur Rajasthan303007 India Department of Computer Engineering and Applications GLA University Uttar Pradesh Mathura281406 India Department of Electronics and Communication Engineering Panimalar Engineering College Tamil Nadu Chennai600123 India Department of Computer Applications Kongu Engineering College Tamil Nadu Perundurai Erode638060 India

A common problem in the banking industry is loan default prediction, which may help identify defaulters who are most likely to stop making payments on their loan payments. Additionally, this information may be used to change the terms and conditions of loans, set aside additional money to guard against potential losses, or even refuse or restrict loans to clients that present a high risk. Although there are numerous conventional techniques for extracting data from loan applications, the majority of these techniques appear to be underperforming, which results in numerous problematic loans. In this proposed model, the detection of defaulter based on Spearman rank correlation with hyper tuned SVM classification is designed for preventing non-performing assets occurring in the bank. For novelty of the model, the missing value replacement is done with the Classification and Regression Tree (CART), which is an imputation as well as classification model that can impute values at missing column based on replacing the different values place at missing place by attaining the best classification accuracy. Then the Z-score normalization and Spearman rank correlation coefficient has been used for normalizing the data and correlation based feature reduction helps to classify then model with low computational complexity. In this hyper tuned SVM model, the traditional SVM model based on grid search cross validation is used for tuning the SVM parameters to accurately detect the defaulter from preventing the non-performing assets occurring in the bank. For evaluating the performance of the proposed loan prediction model, the precision, specificity, accuracy, recall, and Error are 95%, 97%, 96%, 96% and 4%. According to this evaluations, the proposed design performs better than existing model. Thus, the loan defaulter detection using Spearman’s correlation and hyper tuned SVM classification prevents the Non-Performing Assets (NPA) occurring in the bank. © The Author(s), under exclusiv

关键词： CART Defaulter Hyper tuned SVM Loan Spearman rank correlation Z-score

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Enhancing Speech Emotion Recognition Combining Silence Elimination and Attention Model with a Novel CNN Architecture 8th

Enhancing Speech Emotion Recognition Combining Silence Elimi...

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8th International Conference on information and Communication technology for Intelligent Systems, ICTIS 2024

作者： Gawali, Piyush P. Takale, Dattatray G. Deshmukh, Gopal B. Kashid, Shraddha S. Mahalle, Parikshit N. Sule, Bipin Ashokrao, Patil Rahul Derle, Deepak R. Department of Computer Engineering Vishwakarma Institute of Information Technology SPPU Pune Pune India Department of Computer Engineering MIT ADT University Pune India Department of Artificial Intelligence and Data Sciences Vishwakarma Institute of Information Technology SPPU Pune Pune India and Humanities Vishwakarma Institute of Technology Pune India Department of Computer Science and Application KTHM College Nashik India

ISBN: (纸本)9789819766772

A natural interface for human–computer interaction is automatic speech emotion recognition, but it encounters challenges in handling non-emotional speech segments, especially silence, since it is non-emotional speech segmentation that requires recognition. Using a carefully designed attention model and a silence elimination approach, this paper proposes a novel method of enhancing emotion recognition performance by combining silence elimination with attention. In the present study, an enhanced Convolutional Neural Network architecture has been proposed, comprising of nine layers, utilizing a combination of convolutional 1D As a result of the research, CNNs have been employed in a revolutionary speech-emotion detection system that reaches an impressive 96.00% accuracy rate. There is great potential in this breakthrough to develop social robots and conversational robots capable of conveying nuanced human emotions. This breakthrough has marked a significant improvement in accuracy compared to prior models based on the same dataset. In addition to the broader classifications of positive, negative, and neutral emotions, the model's accuracy is evaluated across a wide range of emotion classes, such as anger, calmness, fear, happiness, and sadness for both genders. There is considerable evidence to suggest that combined noise cancellation and attention models are more effective than individual models of noise cancellation or attention. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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C3BAM-XAI: Convolutional Block Attention Module Enhanced Explainable Artificial Intelligence-Based Parkinson’s Disease Stage Classification

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Cognitive Computation 2025年第3期17卷 1-19页

作者： Abbas, Muhammad John Khan, Muhammad Attique Hamza, Ameer Alsenan, Shrooq Rehman, Aleesha Baili, Jamel Zhang, Yudong Department of Computer Science HITEC University Taxila47080 Pakistan Center of Artificial Intelligence College of Computer Engineering and Science Prince Mohammad Bin Fahd University Al-Khobar Saudi Arabia Centre of Real Time Computer Systems Kaunas University of Technology Kaunas51368 Lithuania Information Systems Department College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Pak-Austria Fachhochschule Institute of Applied Sciences and Technology Haripur Mang Pakistan Department of Computer Engineering College of Computer Science King Khalid University Abha61413 Saudi Arabia Department of Informatics University of Leicester Leicester United Kingdom

Parkinson’s disease (PD) is a progressive neurodegenerative disorder that causes significant impairment in neurons, physiological structures, and behavior of people. However, these changes are very subtle in the early stages of PD, making diagnosis and treatment challenging. To overcome such challenges, we proposed a novel convolutional neural network (CNN) architecture called C3BAM-XAI that is based on the convolutional block attention module (CBAM) enhanced CNN and explainable artificial intelligence (XAI). In the proposed architecture, the data imbalance issue was resolved at the initial stage by employing augmentation techniques for the training data. After that, the proposed CNN architecture is designed with CBAM. The CBAM architecture is based on two sub-modules—the channel attention module and the spatial attention module. The input of the initial convolutional layer is passed to the channel attention module, which is further passed to the spatial attention module for more critical information extraction. For smooth training, hyperparameters are selected using Nadam Optimization (NO) instead of manual assignment. In the testing phase, features are extracted from the dense layer and passed to several neural network classifiers. In addition to this, the designed model is interpreted using the explainable AI technique to see the predicted label and insight decision features. The experimental process was performed on the publically available PD Kaggle dataset and obtained an improved accuracy of 93.33%. In addition, several ablation studies were conducted, and it is concluded that the CBAM not only improved accuracy but also made this model a reliable tool for clinical diagnosis, both in terms of accuracy and interpretability. The results thus open new avenues toward applying deep learning models practically for applications in the medical domain and early-stage detection of Parkinson’s disease, thereby helping to improve patient outcomes. © The Author(s), under

关键词： Convolutional neural networks

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MODELING AN INTELLIGENT FRAMEWORK FOR OPTIMIZING UAV PATH PLANNING AND ANTI-COLLISION IN AGRICULTURE

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Scalable Computing 2025年第2期26卷 503-516页

作者： RAO, VIJAYA CHANDRA V KARTHIKEYAN, M.P. SAVITA KUMAR, VENKATA SAIRAM N BHALLA, ANUBHAV SWARUP, LAKSHYA Computer Science and Engineering G. Pullaiah College Of Engineering and Technology India Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Civil Engineering. R.V.R & J.C College of Engineering Guntur India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh 174103 India

Unmanned aerial vehicles (UAV) are increasingly utilized for monitor expansive farming to their effectiveness in observation and data gathered. Efficient path planning and collision prevention are critical for optimizing UAV operation in such settings. The efficiency can be controlled by the quality and declaration of UAV sensed data, as well as the difficulty of real world ecological variables that could impact path optimization and collision prevention. This research introduce a novel technique, the customizable dung beetle search tuned random forest (CDBS-RF) method, designed to improve UAV route planning. The CDBSRF method integrated the dung beetle search (DBS) algorithm, known for its robust optimization capabilities, with random forest (RF) to enhance optimal path security and effectiveness. This method dynamically adjusts path safety and effectiveness. This approach dynamically adjusts path planning parameter to make sure optimal route selection and collision prevention. The suggested technique was evaluated utilizing UAV-sensed data and implement in python based virtual environment. Experimental consequences demonstrate that the CDBS-RF approach considerably enhances UAV path planning performance, provide safer and more efficient navigation for self and more efficient navigation for self-sufficient tarp monitoring. The performance evaluation techniques include the planning time (0.789) and path length (21.526). By utilize advanced optimization and anti-collision algorithms, this technique offer a promising solution for improving UAV operations in agricultural surveillance. © (2025), (West University of Timisoara). All rights reserved.

关键词： Random forests

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Block Verification Mechanism Based on Zero-Knowledge Proof in Blockchain

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computer Systems Science & engineering 2023年第5期45卷 1805-1819页

作者： Jin Wang Wei Ou Osama Alfarraj Amr Tolba Gwang-Jun Kim Yongjun Ren School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410004China Department of Computer Science Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Computer Engineering Chonnam National UniversityGwangju61186Korea School of Computer Science Engineering Research Center of Digital Forensics of Ministry of EducationNanjing University of Information Science&TechnologyNanjing210044China

Since transactions in blockchain are based on public ledger verification,this raises security concerns about privacy *** it will cause the accumulation of data on the chain and resulting in the low efficiency of block verification,when the whole transaction on the chain is *** order to improve the efficiency and privacy protection of block data verification,this paper proposes an efficient block verification mechanism with privacy protection based on zeroknowledge proof(ZKP),which not only protects the privacy of users but also improves the speed of data block *** is no need to put the whole transaction on the chain when verifying block *** just needs to generate the ZKP and root hash with the transaction information,then save them to the smart contract for ***,the ZKP verification in smart contract is carried out to realize the privacy protection of the transaction and efficient verification of the *** the data is validated,the buffer accepts the complete transaction,updates the transaction status in the cloud database,and packages up the ***,the ZKP strengthens the privacy protection ability of blockchain,and the smart contracts save the time cost of block verification.

关键词： Blockchain privacy protection zero-knowledge proof smart contract

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Robust AUC maximization for classification with pairwise confidence comparisons

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Frontiers of computer Science 2024年第4期18卷 73-83页

作者： Haochen SHI Mingkun XIE Shengjun HUANG College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 211106China College of Electronic and Information Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing 211106China

Supervised learning often requires a large number of labeled examples,which has become a critical bottleneck in the case that manual annotating the class labels is *** mitigate this issue,a new framework called pairwise comparison(Pcomp)classification is proposed to allow training examples only weakly annotated with pairwise comparison,i.e.,which one of two examples is more likely to be *** previous study solves Pcomp problems by minimizing the classification error,which may lead to less robust model due to its sensitivity to class *** this paper,we propose a robust learning framework for Pcomp data along with a pairwise surrogate loss called *** provides an unbiased estimator to equivalently maximize AUC without accessing the precise class ***,we prove the consistency with respect to AUC and further provide the estimation error bound for the proposed *** studies on multiple datasets validate the effectiveness of the proposed method.

关键词： method pairwise weakly

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A Study on Cypher Assignment Based on Secrecy Capacity

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International Journal of Network Security 2024年第2期26卷 167-172页

作者： Yang, Cheng-Ying Chen, Jong-Shin Yao, Jenq-Foung J F Hwang, Min-Shiang Department of Computer Science University of Taipei Taipei Taiwan Department of Information and Communication Engineering Chaoyang University of Science and Technology Taiwan Computer Science Georgia College and State University Milledgeville United States Department of Computer Science & Information Engineering Asia University Taiwan Fintech and Blockchain Research Center Asia University Taiwan 500 Lioufeng Rd. Wufeng Taichung41354 Taiwan

Accessing wireless services is convenient but not secure. The issue of privacy and security is highly demanded on the internet. The information is sent with the undesired threats of eavesdroppers. Secure communication enables the desired receiver to correctly and successfully receive the information without interruption. In the meantime, a secure system protects the transmitted information from the eavesdroppers. Traditionally, encryption, authorization, and authentication schemes could be employed to protect from eavesdroppers. However, the unanticipated attacks have been developing. According to Shannon’s perfect communication, the undesired receivers, such as eavesdroppers, could receive an error-free cryptogram. Based on the physical-layer security, it could be expected that secure communication could be implemented without complex computing at the higher level. The physical-layer security coding scheme aims to achieve an extremely low decoding error probability and obtain a critical security level against attackers. The positive secrecy capacity (secrecy rate) could provide secure communication. Hence, a positive secrecy capacity is the primary concern in the system. Besides, physical-layer security coding is not only to help the system achieve a critical level of security but also to obtain an extremely low decoding error probability. However, it decreases the bandwidth efficiency and suffers the system performance. Hence, the tradeoff between the security level and the coding rate is important to lead a secure communication. © (2023), (Femto Technique Co.). All Rights Reserved.

关键词： Bandwidth

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Enhancing IoT-Driven Smart Home Security and Automation with a GCN Model 3

Enhancing IoT-Driven Smart Home Security and Automation with...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Ganti, Sreelakshmi Vankayalapati, Ravi Kumar Krishnamoorthy, P. Thakare, Pallavi S. Nayak, Ullal Akshatha Vignesh, P. Geethanjali College of Engineering and Technology Department of Electronics and Communication Engineering Medchal India Equinix TX United States Sasi Institute of Technology & Engineering Department of Computer Science and Engineering West Godavari India Dr D. Y. Patil Institute of Technology Department of Information Technology Pimpri India Nitte Meenakshi Institute of Technology Department of Information Science and Engineering Bangalore India Kcg College of Technology Department of Mathematics Chennai India

ISBN: (纸本)9798331508456

A network of computing devices and sensors, the Internet of Things (IoT) rapidly exchanges data to assess circumstances and propose new services. Intelligent home technologies rely on IoT. Contemporary smart home systems offer automatic lighting control, smoke detectors, temperature sensors, and smart lock installations. But these technologies raise serious privacy and security concerns. Security flaws let hackers access sensitive data, sendoff false alarms, and take control monitoring systems. Smart home technology is vulnerable to many threats because consumers are wary of it. Recommended steps are preprocessing, sensing, and model training. Preparing raw, unstructured data for analysis. DHT11 humidity and temperature sensors and HC-SR501 PIR motion detectors collect data. It trained the model using Multiscale DGCN data. Better than GCN and BiLSTM, Multiscale DGCN performs. It uses Internet of Things-based smart home security data well, with an average accuracy rate of 93.87%. Identifying outliers in sensor data solves major smart home security challenges. Increasing smart home system accuracy reduces security concerns and boosts user trust, which may drive adoption. © 2025 IEEE.

关键词： Smart homes

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