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Enhancing facial expression recognition in uncontrolled environment: a lightweight CNN approach with pre-processing

Neural Computing and Applications 2025年第10期37卷 7363-7378页

作者： Grover, Richa Bansal, Sandhya Computer Science and Engineering MMU Haryana Mullana India University Institute of Engineering Chandigarh University Punjab Gharuan India

Facial expressions play a key role in human non-verbal type of communication, providing key insights into emotions and intentions. These expressions serve as universal signals, helping individuals convey their internal states across various personal and social contexts. With the growing interest in automatic facial emotion recognition, deep neural networks have emerged as a popular approach for detecting human emotions, even under challenging, real-world conditions. However, external factors can affect the system's performance, degrading the quality of facial features and making emotion detection more difficult. In the presented paper, we propose a highly optimized lightweight convolutional neural network (LCNN) for emotion recognition in controlled and uncontrolled environments. The proposed model is designed to learn hidden nonlinear patterns from facial images. The proposed convolutional neural network consisting a series of convolutional layers followed by max-pooling layers. The model's performance is evaluated with and without pre-processing steps to highlight the importance of pre-processing in improving detection accuracy. The LCNN achieves 65% accuracy on the FER-2013 dataset and 98% on the CK + dataset. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Deep neural networks

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Handwritten optical character recognition using TransRNN trained with self improved flower pollination algorithm (SI-FPA)

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Multimedia Tools and Applications 2025年第18期84卷 19947-19969页

作者： Devi, S. Nandhini Fatima, N. Sabiyath Department of Computer Science and Engineering B.S. Abdur Rahman Crescent Institute of Science & Technology Chennai India

The difficulty of successfully scanning handwritten text arises from variances in style, size, and orientation, which affect handwriting optical character recognition (OCR). This study provides a novel strategy that incorporates innovative techniques for effectively addressing these difficulties. Initially, preprocessing steps involve Gaussian blur to reduce noise and CLAHE to enhance contrast, improving the visibility of text regions. Canny Edge Detection is then utilized for segmentation, followed by text localization using the Efficient and Accurate Scene Text (EAST) algorithm. Next, features are extracted via Inception V3 to capture both low-level and high-level information. From the collected characteristics, Long Short-Term Memory (LSTM) networks are used to extract chronological data. Then, with the help of the Self-Improved Flower Pollination Algorithm (SI-FPA), features are selected using Optimized Principal Component Analysis (PCA). Finally, a combination of Transformer and Recurrent neural networks (RNNs) is used to perform text recognition, maximizing the benefits of each network’s advantages in processing sequential and contextual data. Experiments on MNST dataset with validation using different optimization algorithms show the effectiveness and resilience of the suggested method in different optimization scenarios. The combination of these methods results in a sophisticated OCR system that can recognize handwritten characters with accuracy, even under difficult conditions. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Principal component analysis

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Learner’s intention analysis to mitigate the cold start problem in personalized learning recommendation systems

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Multimedia Tools and Applications 2024年 1-55页

作者： Pal, Saurabh Pramanik, Pijush Kanti Dutta Choudhury, Prasenjit Department of Computer Science and Engineering School of Engineering Brainware University Kolkata India School of Computer Applications and Technology Galgotias University Uttar Pradesh Greater Noida India Department of Computer Science & amp Engineering National Institute of Technology Durgapur West Bengal India

Understanding the learner’s requirements and status is important for recommending relevant and appropriate learning materials to the learner in personalized learning. For this purpose, the learning recommendation system must include the learner’s contextual and behavioral information. However, in the case of cold start problems, the system has no prior information about a new user. Moreover, the system cannot acquire the required information instantly because the automated and instantaneous acquisition of this information is not trivial. This hinders the personalized recommendation for a new learner. Engaging the learner in a questionnaire session may allow the learning system to acquire most of the required information. However, this makes the learner uninterested and disengaged with the system. To address this, we propose a recommendation system for personalized learning that mitigates the cold start problem by acquiring important information by analyzing the learner’s questions submitted to the learning system. By analyzing the meta-information of a question, we identified the learner’s exact requirements, domain and topic knowledge, and expected knowledge gain from the learning material. Ontology, BiLSTM, and context mapping are used for learner modelling and topic classification, and identifying suitable learning materials, respectively. Furthermore, MCDM techniques are used for ranking the learning materials. Our experimental assessment and validation suggest that the information acquired based on our proposed approach is sufficient for understanding a new learner’s needs and suitability and, accordingly, for recommending suitable personalized learning materials. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Recommender systems

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Breast Cancer Classification Using Graph Convolutional Networks and DenseNet121 with Pruning

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Journal of Shanghai Jiaotong University (science) 2025年 1-18页

作者： Kavitha, S.J. Sridevi, S. Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies Tamil Nadu Chennai600117 India

Breast cancer remains a significant global health burden, particularly among women, necessitating accurate and accessible diagnostic systems for early detection and intervention. Despite advancements in medical technology, challenges persist such as high dimensionality, variability, and complexity. Hence, this study introduces a novel approach that amalgamates DenseNe5t121 architecture within graph convolutional networks (GCNs) for breast cancer classification. The proposed method combines the strengths of a pre-trained DenseNet121 model, pruning techniques, and GCN with a cross-space filter (CSF). DenseNet121 is employed for feature extraction, while pruning enhances efficiency and prevents overfitting. GCNs capture structural and semantic information from the data and the CSF aids in regularization. Furthermore, the incorporation of the CSF enhances model performance by effectively integrating information from both graph topology and node attributes. The proposed approach was trained and evaluated using the BreakHis dataset, a widely recognized resource for breast cancer classification through histopathological images. By leveraging several performance measures consisting of precision, recall, F1-score, AUC, MCC, and accuracy, the proposed approach was compared against existing models to assess its effectiveness. The results indicate that the proposed method, particularly when combined with the CSF, outperforms existing approaches, demonstrating its potential as an advanced tool for accurate and interpretable breast cancer diagnosis. (Figure presented.) © Shanghai Jiao Tong University 2025.

关键词： Diseases

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Classification Model for IDS Using Auto Cryptographic Denoising Technique

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computer Systems science & engineering 2023年第4期45卷 671-685页

作者： N.Karthikeyan P.Sivaprakash S.Karthik Department of Computer Science&Engineering PPG Institute of TechnologyCoimbatoreTamilnaduIndia Department of Computer Science&Engineering SNS College of TechnologyCoimbatoreTamilnaduIndia

Intrusion detection systems(IDS)are one of the most promising ways for securing data and networks;In recent decades,IDS has used a variety of categorization *** classifiers,on the other hand,do not work effectively unless they are combined with additional algorithms that can alter the classifier’s parameters or select the optimal sub-set of features for the *** are used in tandem with classifiers to increase the stability and with efficiency of the classifiers in detecting *** algorithms,on the other hand,have a number of limitations,particularly when used to detect new types of *** this paper,the NSL KDD dataset and KDD Cup 99 is used to find the performance of the proposed classifier model and compared;These two IDS dataset is preprocessed,then Auto Cryptographic Denoising(ACD)adopted to remove noise in the feature of the IDS dataset;the classifier algorithms,K-Means and Neural network classifies the dataset with adam *** classifier is evaluated by measuring performance measures like f-measure,recall,precision,detection rate and *** neural network obtained the highest classifying accuracy as 91.12%with drop-out function that shows the efficiency of the classifier model with drop-out function for KDD Cup99 *** their power and limitations in the proposed methodology that could be used in future works in the IDS area.

关键词： Auto cryptographic denoising(ACD) classifier intrusion detection system(IDS) optimizer performance measures

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Hash-Indexing Block-Based Deduplication Algorithm for Reducing Storage in the Cloud

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computer Systems science & engineering 2023年第7期46卷 27-42页

作者： D.Viji S.Revathy Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyChennaiIndia

Cloud storage is essential for managing user data to store and retrieve from the distributed data *** storage service is distributed as pay a service for accessing the size to collect the *** to the massive amount of data stored in the data centre containing similar information and file structures remaining in multi-copy,duplication leads to increase storage *** potential deduplication system doesn’t make efficient data reduction because of inaccuracy in finding similar data *** creates a complex nature to increase the storage consumption under *** resolve this problem,this paper proposes an efficient storage reduction called Hash-Indexing Block-based Deduplication(HIBD)based on Segmented Bind Linkage(SBL)Methods for reducing storage in a cloud ***,preprocessing is done using the sparse augmentation ***,the preprocessed files are segmented into blocks to make *** block of the contents is compared with other files through Semantic Content Source Deduplication(SCSD),which identifies the similar content presence between the *** on the content presence count,the Distance Vector Weightage Correlation(DVWC)estimates the document similarity weight,and related files are grouped into a ***,the segmented bind linkage compares the document to find duplicate content in the cluster using similarity weight based on the coefficient match *** implementation helps identify the data redundancy efficiently and reduces the service cost in distributed cloud storage.

关键词： Cloud computing deduplication hash indexing relational content analysis document clustering cloud storage record linkage

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Enhanced Wi-Fi-Li-Fi system

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International Journal of Wireless and Mobile Computing 2023年第2期25卷 183-189页

作者： Gowtham, M. Department of Computer Science and Engineering The National Institute of Engineering Karnataka Mysuru India

Today's world is fully dependent on data. Data are individual packets or units of information which on process leads to a useful information which intend helps in decision making. So these data are to be shared among the multiple user devices and we come across some of the most commonly used transmission media. Currently, Wireless Fidelity (Wi-Fi) is dominating over other media because of its ease of remote accessing methods without any physical connection. But these do have some loopholes, as they have limited speed of transmission or very low bit rate. So in order to overcome these drawbacks we come up with an emerging concept where the speed of transmission can be enhanced up to 100 Gbps with the help of light as the source of transmission, called Light-Fidelity (Li-Fi). Li-Fi range is of large bandwidth that enables the communication in radiofrequency areas and shows data transmission efficiently. By using both Wi-Fi and Li-Fi we propose a new system, the hybrid Wi-Fi-Li-Fi system. © 2023 Inderscience Enterprises Ltd.

关键词： Decision making

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A Comprehensive Analysis of Feature Selection Techniques and Their Impact on Earthquake Damage Prediction

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Iranian Journal of science and Technology - Transactions of Civil engineering 2025年 1-35页

作者： Feroz, Shejuti Binte Sharmin, Nusrat Department of Computer Science and Engineering Military Institute of Science and Technology Dhaka Bangladesh

Earthquake damage prediction is vital to ensure occupants of buildings are not injured and substantial financial losses can be avoided. Algorithms based on machine learning are prevalent in this field. This study conducts an in depth analysis of the impact of feature selection on machine learning models for earthquake damage prediction. A feature selection method is critical for reducing data dimensionality, improving model accuracy, and mitigating overfitting. The Bat Algorithm, Particle Swarm Optimization, and Simulated Annealing were evaluated alongside twelve traditional feature selection techniques, such as Information Gain, Chi-square Test, Fisher’s Score, and LASSO Regularization. A comprehensive dataset from the 2015 Gorkha earthquake in Nepal was used to assess the predictive performance of three machine learning models-Decision Tree, Random Forest, and LightGBM. The novelty of this work lies in applying a broad spectrum of feature selection methods—many of which are being used for the first time—on the real-world dataset from the 2015 Gorkha earthquake in Nepal. A significant role is played by feature selection in the improvement of model accuracy and efficiency. Out of all the models, the Forward Feature Selection and Correlation Coefficient showed the best trade-off between processing time and accuracy. This study contributes to future disaster preparedness and structural resilience initiatives by highlighting the significance of meticulous feature selection and providing a useful framework for improving earthquake damage prediction models. © The Author(s), under exclusive licence to Shiraz University 2025.

关键词： Deep learning Earthquake prediction Machine learning

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mLBOA-DML: modified butterfly optimized deep metric learning for enhancing accuracy in intrusion detection system

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Journal of Reliable Intelligent Environments 2023年第3期9卷 333-347页

作者： Prabhakaran, Varun Kulandasamy, Ashokkumar Department of Computer Science and Engineering St.Joseph’s College of Engineering Chennai India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

Intrusion detection is a prominent factor in the cybersecurity domain that prevents the network from malicious attacks. Cloud security is not satisfactory for securing the user’s information because it is based on standard protocols. Hence, cloud users cannot fully trust the security offered by cloud service providers. The state-of-the-art techniques create clusters for classes and manually label the unknown classes to detect novel attacks. This notion binds the network traffic associated with each attack together and drifts the similarity between the same attacks. These techniques are often prone to errors and degrade performance. To overcome this drawback, various researchers have developed different intrusion detection system which relies on specific attack patterns to distinguish between normal and abnormal behavior. This paper presents a modified Lagrange interpolated Butterfly optimization algorithm-based deep metric learning (mLBOA-DML) architecture for intrusion detection to detect both host-based and network attacks. DML architecture parameters are optimized utilizing mLBOA algorithm via its global optimization capability for increasing the accuracy of attack prediction. DML algorithm does both feature extraction and classification processes. When evaluated using the UNSW-NB15 and NSL-KDD datasets, the proposed model offers improved accuracy near 99% for both datasets. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Intrusion detection

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Block Chain Based Voting System 2

Block Chain Based Voting System

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2nd IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, ICWITE 2024

作者： Patil, Vijeeta Bagalkot, Suhasini Rohit Guru, K.M. Kle Institute of Technology Computer Science and Engineering India Rrce Bangalore Computer Science and Engineering India

ISBN: (纸本)9798350383287

As we as a whole realize that casting ballot extortion is basic in India and furthermore in many created nations as well. There have been a few activities to lessen casting a ballot misrepresentation, for instance, the utilization of EVM machines in India. In the event that we can utilize some innovation to ensure against something similar. Here comes the utilization of Blockchain, a blockchain is a protected conveyed record that is constant and utilizes cryptographic techniques to execute the given properties. In this task, we plan a proposition for setting up the best answer for a blockchain casting a ballot decentralized applications(DApp) that consolidates citizen self-sovereign-ID and undeniable voting form arrangement. We will utilize brilliant agreements to execute the standards set up by the Election commission of India. Our proposition tends to the essential standards and shows key capacities and contemplations for a completely supported drive and improved elector end up, progressing commitment, and tremendous expense investment funds by limiting blunders and manual information passage and framework organization for heritage frameworks. We accept that an electronic democratic DApp ought to be reason-driven, the local area situated, in view of open guidelines for blockchain advancements, citizen protection, and security as a goal, and autonomous check and straightforwardness for polling from the organization. © 2024 IEEE.

关键词： Smart contract

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