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2nd NIELIT's International Conference on Communication, Electronics and Digital Technologies, NICEDT 2024

作者： Kohli, Mudita Chhabra, Indu Department of Computer Science and Applications Panjab University Chandigarh India

ISBN: (纸本)9789819736003

Anomalous network packet detection is a dire security concern today. ChatGPT era has emerged as a grave concern for computer scientists and programmers. The method of recognizing diverged network activity from the typical behaviour of network flows is known as anomalous network packet detection. As networks have expanded in terms of complexity and size, detecting outlier occurrences in such huge and complex networks has become crucial in retaining integrity. Detecting threats quickly and accurately determines the success of any network flow. To ensure reliable behaviour and capacity to model features, deep learning models have emerged as promising tools in recent years. Developing an efficient anomaly detection system necessitates understanding the imbalanced datasets, recognizing changing patterns and identifying irregularities. The paper outlines attacks, knowledgeable parameters, techniques and datasets for studying crucial network anomalies by reviewing research for different periods of analysis—before 2000, 2000–2010, 2011–2015, 2016–2018 and 2019–2023. The challenges are addressed, and futuristic options for anomaly detection systems are proposed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Anomaly detection

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Evaluating Performance of SMOTE and ADASYN to Classify Falls and Activities of Daily Living 12th

Evaluating Performance of SMOTE and ADASYN to Classify Falls...

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12th International Conference on Soft Computing for Problem Solving, SocProS 2023

作者： Kaur, Rajbinder Sharma, Rohini Dhaliwal, Manpreet Kaur Department of Computer Science and Applications Panjab University Chandigarh India

ISBN: (纸本)9789819732913

Class Real-life applications generate skewed data that leads to degraded performance of machine learning models. These methods are biased towards the majority class and cause overfitting problems. Oversampling techniques are used by researchers to balance the dataset by increasing minority class samples which increases the performance of the classifier. Detecting falls and activities of daily living accurately improves the quality of life, particularly for elderly people. In this study, real-time data pertaining to falls and activities are collected using a working prototype. Experiments are performed to balance the data using SMOTE and ADASYN oversampling techniques. Three machine learning models, KNN, RF and CART, with and without parameter tuning, are used for the classification of different types of activities and falls. The results prove that ADASYN and SMOTE improve the accuracy of classification and ADASYN performs better than SMOTE. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Machine learning

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Real-Time Face Detection on Security Surveillance Footage

Real-Time Face Detection on Security Surveillance Footage

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2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Mohd, Noor Dham, Shradhay Bisht, Jai Singh Mehta, Aryan Joshi, Akshita Martha, Sheshikala Department of Computer Science and Engineering Dehradun India Department of Computer Applications Dehradun India Sr University Department of Computer Science and Artificial Intelligence Warangal India

ISBN: (纸本)9798350379716

Recent years have witnessed notable progressions in facial recognition technology which have been led by the inception of deep learning models-primarily Siamese Neural Networks. This article delves into the use of Siamese Neural Networks for one-shot face classification tasks, giving priority to extracting discriminative features and training deep learning models. Among the pillars of our methodology are: data collection;preprocessing;model building and real-time verification using deep learning abilities, especially Convolutional Neural Networks (CNNs). We hope that through this approach we can significantly improve the accuracy of facial recognition in real-time surveillance applications with what CNNs have to offer. © 2024 IEEE.

关键词： Convolutional neural networks

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Lightweight key distribution for secured and energy efficient communication in wireless sensor network:An optimization assisted model

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High-Confidence Computing 2023年第2期3卷 40-49页

作者： Ezhil Roja P. Misbha D.S. Department of Computer Science Nesamony Memorial Christian CollegeMarthandam 629165India Department of Computer Science and Applications Nesamony Memorial Christian CollegeMarthandam 629165India

Due to their open,expansive,and resource-constrained character,Wireless Sensor Networks(WSNs)face significant energy,efficiency,and security *** a fair level of energy and resource consumption,several lightweight cryptographic techniques are introduced to increase the security as well as effectiveness of ***,they have problems with scalability,key distribution,security,and power *** work proposes a novel light weighted key distribution mechanism for safe and energy efficient communication in *** proposed model includes stages like optimal Cluster Head Selection(CHS),improved Elliptic Curve Cryptography(ECC)-based encryption,and lightweight key management,*** the first phase,a hybrid optimization strategy is proposed,termed as Coot updated Butterfly algorithm with Logistic Solution Space algorithm(CUBA-LSS)for optimal clustering via selecting the optimal *** selection process relies on the consideration of the Received Signal Strength Indicator(RSSI),energy,delay,and *** transmission is the subsequent process,where the proposed algorithm ensures secured transmission through Improved *** last,a lightweight key management system is determined via session key generation to protect the encryption key.

关键词： WSN Cluster head RSSI Elliptic curve CUBA-LSS optimization

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An Approach for Detecting and Restoring Tampering in Digital Image Watermarking 5th

An Approach for Detecting and Restoring Tampering in Digital...

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5th International Conference on Cognitive Computing and Information Processing, CCIP 2023

作者： A. C., Pavan Somashekar, M.T. Department of Computer Science and Applications Bangalore University Bengaluru India

ISBN: (纸本)9783031607240

In addition to ensuring image authenticity, watermarking aids in the recovery of digital images that have been altered or tampered with during transmission. Although many techniques have been developed for embedding the watermark that results in recovering itself of photos, they only effectively work in the uncompressed multiple domains, leaving images vulnerable to attacks on image compression, such as noise addition. In order to effectively detect tampered areas and assure self-recovery of JPEG images, we offer a solution to address these issues in the JPEG domain. Source code compresses the original image, while channel code protects it from tampering. As a result, the image will be compressed and then used as the watermark which is embedded. The approach proposed is successfully combined with these JPEG images, producing increased image quality as well as strong performance against noise attacks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Image compression

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Medical Imagechain by Integration of Convolutional Neural Network and Blockchain to Secure Medical Image Storage 4

Medical Imagechain by Integration of Convolutional Neural Ne...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Maheswari, K.P. Kalaiselvi, T. The Gandhigram Rural Institute - Deemed to be University Department of Computer Science and Applications Dindigul India Fatima College Department of Computer Applications Madurai India

ISBN: (纸本)9798350352931

The Medical Imagechain integrates blockchain technology and Convolutional Neural Networks (CNN) to create secure, decentralized system for managing medical images. Focusing on Slices of Interest (SoI) from MRI brain tumor datasets, it enhances efficiency and accuracy. Blockchain ensures data integrity and security, while CNNs reduce computational demands by identifying relevant slices. A customized Proof of Work (PoW) consensus algorithm with varying difficulty levels and hashing techniques validates and appends new blocks to the blockchain. This system is developed using 3,000 MRI brain images, achieved 95% validation and 96% training accuracy. Cryptographic techniques further enhance data security throughout the chaining process. This study highlights the potential of combining CNN with blockchain technology to transform healthcare, which guarantees the safe and effective handling of medical images, especially for targeted slices. The Medical Imagechain system highlights the feasibility of leveraging these technologies for improved medical imaging workflows. © 2024 IEEE.

关键词： Convolutional neural networks

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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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Aggregated Relative Similarity (ARS): a novel similarity measure for improved personalised learning recommendation using hybrid filtering approach

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Multimedia Tools and applications 2024年 1-48页

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

To improve the effectiveness of online learning, the learning materials recommendation is required to be personalised to the learner material recommendations must be personalized to learners. The existing approaches are ineffective in recommending learning materials according to the learner’s learning situation, the learner’s internal learning characteristics, and fitting to the learner’s preferences and learning suitability. In this work, we propose a hybrid recommendation approach cthat combines the advantages of knowledge-based and collaborative filtering approaches. The knowledge-based filtering approach enables us to find the most suitable learning materials that fit the learner’s situation and learning state by appropriately mapping the learners’ contexts with the metadata of the learning materials. We use collaborative filtering to refine learning material selection by considering learner-learner similarity. We propose a novel similarity measure, aggregated relative similarity (ARS), to determine similar learners based on implicit learner characteristics such as education, knowledge, cognitive ability, and learning style. The experimental results show that the proposed ARS algorithm outperforms popular similarity measures such as Jaccard similarity, Sorensen-Dice coefficient, and overlap coefficient. The overall recommendation performance of the proposed hybrid approach attained mean absolute error (MAE) and root mean square error (RMSE) of 0.681 and 0.9198, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Collaborative filtering

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A deep convolutional neural network-based keratitis detection and classification approach

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Multimedia Tools and applications 2025年 1-21页

作者： Susila, S. Janet Grace Kavitha, D. Department of Computer Science St. Peter’S Institute of Higher Education and Research Chennai India Department of Computer Applications St. Peter’S Institute of Higher Education and Research Chennai India

Keratitis is an intraocular disease that is an inflammation of the cornea and one of the challenges of ophthalmology due to its multifarious character and mild initial symptoms. In this paper, we present KeraNet Deep Convolutional Neural Network (DCNN), which is a novel deep learning architecture to aid the early detection of keratitis. Our proposed model adds on a hybrid attention mechanism that employs spatial and channel attention to guide the model’s focus to the relevant parts of the cornea, along with adaptive multi-scale feature extraction, which helps in capturing the features of lesions of different sizes. The architecture uses Hierarchical Dense Residual Blocks (HDRBs) to improve feature extraction without the problem of vanishing gradients. Deformable Convolutions, on the other hand, have the advantage of changing receptive fields in order to successfully configure the abnormal formations. In our experiments, KeraNet DCNN showed a high balanced accuracy of 98.4% and a sensitivity of 98.7%, which is better performance than all existing approaches, including EfficientNetV2-M and OL-CDACNN, in terms of sensibility and specificity. The image analysis tools capabilities were further tested on a new model containing a loss that combines the localization of lesions and the prediction, hence optimally addressing the problem of prediction accuracy and interpretation. Having undergone extensive testing and evaluation, KeraNet DCNN shows good promise in the detection of keratitis at its earliest stage;this is a revolutionary breakthrough in the field of automated diagnosis in ophthalmology. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Ophthalmology

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A Comprehensive Analysis of Knowledge Graph-based Social Recommendation System 1

A Comprehensive Analysis of Knowledge Graph-based Social Rec...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Navuluri, Madhavilatha Bhuyan, Hemanta Kumar RGUKT-IIIT Department of Computer Science and Engineering Ongole India Department of Information Technology and Computer Applications Guntur India

ISBN: (纸本)9798331529246

Recommendation systems are crucial due to their high relevance in terms of interpretability and performance. A Social Recommendation system explores how social relations influence user choices and how users select items. In this system, user-embeddings are learned from Knowledge graphs to enhance the recommendations. The social recommendation system can be categorized into three main areas: user recommendation, movie recommendation, and explainable recommendation. User recommendation focusses on providing users with personalized services, movie recommendation enhances the movie-watching experience, and explainable recommendation aims to provide the reasoning behind the suggested items. Data sparsity and the cold start problem are significant challenges faced by RS. The Social RS model addresses these issues by leveraging both user-item interactions and user-user social relationships, thereby improving the overall quality of social network-based recommendations. Methods such as Light Graph Convolutional Network (LGCN), Hyperbolic Recommender (HSR), and Knowledge Enhanced Graph Neural Networks (KEGNN) have been proposed to enhance social recommendation performance. Evaluation metrics such as recall @N, precision @N are commonly used to assess and estimate the effectiveness of recommendation systems. © 2025 IEEE.

关键词： Knowledge graph

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