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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Swarna Sudha, M. Manjula, S. Bharathi, Indira Krishnasamy, Valarmathi Vijayalakshmi, K. Ramco Institute of Technology Department of Computer Science and Engineering Rajapalayam India School of Computer Science and Engineering Vellore Institute of Technology Chennai India PSR Engineering College Department of Electronics and Communication Engineering Sivakasi India

ISBN: (纸本)9798331523923

This research introduces DeepFakeGuard, a hybrid deep learning framework designed to detect fake profiles on social media platforms, addressing the growing threat of fraudulent accounts online. DeepFakeGuard integrates multiple data modalities, including textual, visual, behavioral, and network features, to accurately classify profiles as real or fake. Leveraging advanced deep learning models such as BERT for textual analysis, ResNet for image processing, and LSTM for modeling behavioral patterns, DeepFakeGuard provides a comprehensive and robust solution for identifying fake profiles across diverse platforms. The framework processes a rich set of profile characteristics, including account information, posting behavior, post sentiment, and network relationships, ensuring a multi-dimensional analysis of social media accounts. The hybrid architecture of DeepFakeGuard merges outputs from individual sub-models, which are then processed through dense layers for final classification using a sigmoid activation function. This multi-faceted approach significantly enhances detection accuracy, precision, and recall compared to traditional methods. The framework is highly scalable and adaptable to various social media platforms, offering a versatile tool for strengthening online security. With strong performance metrics, including high classification accuracy, DeepFakeGuard demonstrates its potential for real-world applications in mitigating the risks associated with fake profiles. By fostering secure and trustworthy social media environments, this research contributes to the ongoing fight against fraudulent accounts, paving the way for safer online interactions. © 2025 IEEE.

关键词： Contrastive Learning

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An Experimental Analysis of Machine Learning Models for Diabetes Classification 7th

An Experimental Analysis of Machine Learning Models for Di...

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7th International Conference on Communication, Device and Networking, ICCDN 2024

作者： Ghosh, Subhayu Acharya, Riyan Jana, Nanda Dulal Department of Computer Science and Engineering National Institute of Technology Durgapur West Bengal Durgapur India Department of Computer Science and Engineering SRM Institute of Science and Technology Tamil Nadu Kattankulathur India

ISBN: (纸本)9789819764648

Diabetes is a chronic metabolic disorder that affects millions of people worldwide. Early detection and effective management of diabetes are crucial to prevent severe complications and improve the quality of life for affected individuals. Machine Learning (ML) techniques have shown great promise in aiding the early detection of various diseases on patient data, including diabetes. ML algorithms can analyze vast datasets, identify patterns, and make accurate predictions, helping medical professionals to diagnose diabetes at its early stages. In our work, we employed several ML models for diabetes classification using different datasets. These models include K-Nearest Neighbor (KNN), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Gradient-Boosted Model (GBM), eXtreme Gradient Boosting (XG-Boost), Adaptive Boosting (Ada-Boost), Support Vector Machine (SVM), and Gaussian Naive Bayes (GNB). We performed a comparative analysis of their performance on three distinct datasets using evaluation metrics like accuracy, precision, F1-score, sensitivity, specificity and Cohen’s Kappa Value. Our findings revealed that the RF algorithm is optimal for symptoms-based and primary lab report-based diabetes detection, while XG-Boost excels in classifying different types of diabetes from a multi-class dataset. Moreover, we investigated diverse symptoms and their impact on diabetes outcomes, offering insights into preventive measures and early stage monitoring for this disease classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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A comprehensive review on detection of depression through social media analysis 4th

A comprehensive review on detection of depression through so...

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4th International Conference on Computational Methods in Science and technology, ICCMST 2024

作者： Dogra, Shriya Kaur, Parneet Sharma, Tejpal Kumari, Nisha Department of Computer Science and Engineering Chandigarh Engineering College-CGC Landran India Department of Computer Science and Engineering Chitkara University Institute of Engineering and Technology Punjab India

ISBN: (纸本)9781032911571

Social media platforms like Instagram, Twitter, and Facebook have completely changed our world. People today exhibit a kind of digital character and are more linked than ever. While social media undoubtedly offers many noteworthy advantages, there are also unavoidable drawbacks. Later Research has demonstrated a link between elevated use of social media platforms and heightened levels of depression. The goal of the current study is to study the existing machine learning approaches to identify potentially sorrowful Twitter users based on their tweets and network behaviour. To accomplish this, we assessed classifiers that identify whether or not a person is depressed based on information extracted from their tweets and network behaviour. It is observed that as a greater number of features are incorporated, the F-score and accuracy in classifying depressed persons also improve. This technique is a predictive, data-driven tool for early mental health diagnosis of depression or any type of psychiatric ailments. The main contribution of the article is the examination of the traits and how they affect the ability to gauge depression severity. © 2025 the Author(s).

关键词： Tweets

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Improved Load-Balanced Clustering for Energy-Aware Routing(ILBC-EAR)in WSNs

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computer Systems Science & engineering 2023年第1期44卷 99-112页

作者： D.Loganathan M.Balasubramani R.Sabitha S.Karthik Department of Electronics and Communication Engineering Info Institute of EngineeringCoimbatore641107India Department of Computer Science and Engineering SNS College of TechnologyCoimbatore641035India

Sensors are considered as important elements of electronic *** many applications and service,Wireless Sensor Networks(WSNs)are involved in significant data sharing that are delivered to the sink node in energy efficient man-ner using multi-hop ***,the major challenge in WSN is the nodes are having limited battery resources,it is important to monitor the consumption rate of energy is very much ***,reducing energy con-sumption can increase the network lifetime in effective *** that,clustering methods are widely used for optimizing the rate of energy consumption among the sensor *** that concern,this paper involves in deriving a novel model called Improved Load-Balanced Clustering for Energy-Aware Routing(ILBC-EAR),which mainly concentrates on optimal energy utilization with load-balanced process among cluster heads and member *** providing equal rate of energy consumption among nodes,the dimensions of framed clusters are ***,the model develops a Finest Routing Scheme based on Load-Balanced Clustering to transmit the sensed information to the sink or base *** evaluation results depict that the derived energy aware model attains higher rate of life time than other works and also achieves balanced energy rate among head ***,the model also provides higher throughput and minimal delay in delivering data packets.

关键词： Wireless sensor networks energy consumption load balanced clustering finest routing

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FusionEXNet: an interpretable fused deep learning model for skin cancer detection

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International Journal of computers and Applications 2024年第9期46卷 743-753页

作者： Gautam, Yuvika Gupta, Piyush Kumar, Deepika Kalra, Bhavya Kumar, Abhinav Hemanth, Jude D. Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering New Delhi India Department of Electronics & Communication Engineering Karunya Institute of Technology and Sciences Coimbatore India

Skin cancer poses a significant burden on mankind and healthcare systems globally, necessitating the development of effective diagnostic and treatment strategies. This paper introduces FusionEXNet, an innovative and interpretable fused deep-learning model for skin cancer detection. FusionEXNet leverages the strengths of EfficientNetV2S and XceptionNet architectures to extract robust features from dermoscopic images, achieving superior performance compared to individual models. While XceptionNet and EfficientNetV2S attained accuracies of 88.82% and 88.01%, respectively, FusionEXNet surpassed them with an impressive accuracy of 90.83%. To enhance model interpretability, Explainable Artificial Intelligence (XAI) techniques, such as SmoothGrad and Faster Score-CAM, are integrated, providing valuable insights into the decision-making process. The model was trained and evaluated using the extensive HAM10000 dataset, consisting of over 10,000 high-resolution images across seven skin lesion categories. The proposed FusionEXNet model offers a reliable, accurate, and interpretable system for skin cancer detection, contributing to improved patient outcomes and more efficient diagnostic processes. This study underscores the potential of combining advanced CNN architectures with XAI methods to create powerful tools for dermatological diagnostics. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Deep learning

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Differently processed modality and appropriate model selection lead to richer representation of the multimodal input

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International Journal of Information technology (Singapore) 2024年第7期16卷 4505-4516页

作者： Panda, Saroj Kumar Diwan, Tausif Kakde, Omprakash G. Department of Computer Science and Engineering Indian Institute of Information Technology Tata Consultancy Services

We aim to effectively solve and improvise the Meta Meme Challenge for the binary classification of hateful memes detection on a multimodal dataset launched by Meta. This problem has its challenges in terms of individual modality processing and its impact on the final classification of hateful memes. We focus on feature-level fusion methodologies in proposing the solutions for hateful memes detection in comparison with the decision-level fusion as feature-level fusion generates richer features’ representation for further processing. Appropriate model selection in multimodal data processing plays an important role in the downstream tasks. Moreover, inherent negativity associated with the visual modality may not be detected completely through the visual processing models, necessitating the differently processed visual data through some other techniques. Specifically, we propose two feature-level fusion-based methodologies for the aforesaid classification problem, employing VisualBERT for the effective representation of textual and visual modality. Additionally, we employ image captioning generating the textual captions from the visual modality of the multimodal input which is further fused with the actual text associated with the input through the Tensor Fusion Networks. Our proposed model considerably outperforms the state of the arts on accuracy and AuROC performance metrics. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Deep learning Hateful memes Multimodality Text tensor fusion VisualBERT

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Fusion of Wavelet Decomposition and N-BEATS for Improved Stock Market Forecasting

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SN computer Science 2024年第7期5卷 1-22页

作者： Pramanick, Neha Singhal, Vatsal Neeraj Mathew, Jimson Agarwal, Mayank Department of Computer Science and Engineering Indian Institute of Technology Patna Patna 801103 India

Stock market forecasting is one of the most exciting areas of time series forecasting both for the industry and academia. Stock market is a complex, non-linear and non-stationary system with many governing factors and noise. Some of these factors can be quantified and modeled, whereas some factors possess a random walk behavior making the process of forecasting challenging. Various statistical methods, machine learning, and deep learning techniques are prevalent in stock market forecasting. Recently, there has been a paradigm shift towards hybrid models, showing some promising results. In this paper, we propose a technique that combines a recently proposed deep learning architecture N-BEATS with wavelet transformation for improved forecasting of future prices of stock market indices. This work uses daily time series data from five stock market indices, namely NIFTY 50, Dow Jones Industrial Average (DJIA), Nikkei 225, BSE SENSEX, and Hang Seng Index (HSI), for the experimental studies to compare the proposed technique with some traditional deep learning techniques. The empirical findings suggest that the proposed architecture has high accuracy as compared to some traditional time series forecasting methods and can improve the forecasting of non-linear and non-stationary stock market time series. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： 00-01 99-00 Deep learning Forecasting N-BEATS Stock market Time series

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Lightweight Convolutional Neural Network Model for Cassava Leaf Diseases Classification

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SN computer Science 2024年第3期5卷 284页

作者： Tewari, Anand Shanker Computer Science and Engineering Department National Institute of Technology Patna Patna 800005 India

The world population has increased many folds in the last few years and crossed the figure of 7 billion. Africa has the highest population growth rate. Food and water are the first and foremost necessities for the survival of living beings. Cassava is among the staple foods of Africa and other countries. Its roots and leaves fulfill the daily caloric demands of millions of people. In the last few years, the production of cassava has decreased due to the spread of disease in cassava leaves. The manual identification of these diseases needs large number of people with sufficient level of skills in this field, so an automated technique is required that can help farmers by detecting cassava leaf diseases within a small time frame. The paper proposed a lightweight convolutional neural network (CNN) model to find four types of cassava leaf diseases i.e., cassava mosaic disease, cassava green mottle, cassava bacterial blight, and cassava brown streak leaf disease. It uses depthwise separable convolution operation to reduce the number of parameters that makes it suitable for mobile devices. The proposed model has used both channel attention and spatial attention to highlight disease part of the leaf and suppress background scenes. The modified attention mechanism of the proposed work helps in further improving the accuracy of the model. The experimental results exhibit that the proposed model outperforms other state-of-the-art models like VGG16, ResNet 50, EfficientNet, MobileNet V1, and MobileNetV2. The proposed model uses 1.1 million fewer parameters than MobileNet V2 that makes it suitable for low computing power devices. It uses the natural scene images for the classification that enhances its applicability in real-world scenarios. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Attention Convolutional neural network Deep learning Leaf disease classification

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Credit Card Fraud Detection Using Machine Learning 2

Credit Card Fraud Detection Using Machine Learning

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Peneti, Subhashini Rama Krishna, S. Kiran, Ajmeera Tripathy, Hrudaya Kumar Mlr Institute of Technology Department of Computer Science & Information Technology Hyderabad India Gitam University Department of Computer Science & Engineering Hyderabad India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India Kalinga Institute of Industrial Technology Deemed to Be University India

ISBN: (纸本)9798350370188

India's banking sector has grown rapidly in modern times. With increased bank functioning, frauds skyrocket. Debit cards, Internet Banking, and ATM cards are among the financial services offered today. These services attracted many new customers worldwide. We present a Machine Learning technique to detect credit card fraud. The global use of credit cards is expanding significantly. Thus, the bank server can track these transactions using Machine Learning. These algorithms detect or anticipate transaction fraud. Each transaction has a few functions in the dataset. We should classify and identify fraudulent transactions from those transactions. The work suggests using Machine Learning calculation for this. © 2024 IEEE.

关键词： Crime

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Towards a blockchain-SDN-based secure architecture for cloud computing in smart industrial IoT

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Digital Communications and Networks 2023年第2期9卷 411-421页

作者： Anichur Rahman Md Jahidul Islam Shahab S.Band Ghulam Muhammad Kamrul Hasan Prayag Tiwari Department of Computer Science and Engineering National Institute of Textile Engineering and Research(NITER)Constituent Institute of the University of DhakaSavarDhaka1350Bangladesh Department of Computer Science and Engineering Green University of BangladeshDhakaBangladesh Future Technology Research Center College of FutureNational Yunlin University of Science and TechnologyDouliouTaiwanChina Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia Department of Information and Communication Engineering Hankuk University of Foreign StudiesSouth Korea Department of Computer Science Aalto UniversityFinland

Some of the significant new technologies researched in recent studies include BlockChain(BC),Software Defined Networking(SDN),and Smart Industrial Internet of Things(IIoT).All three technologies provide data integrity,confidentiality,and integrity in their respective use cases(especially in industrial fields).Additionally,cloud computing has been in use for several years *** information is exchanged with cloud infrastructure to provide clients with access to distant resources,such as computing and storage activities in the *** are also significant security risks,concerns,and difficulties associated with cloud *** address these challenges,we propose merging BC and SDN into a cloud computing platform for the *** paper introduces“DistB-SDCloud”,an architecture for enhanced cloud security for smart IIoT *** proposed architecture uses a distributed BC method to provide security,secrecy,privacy,and integrity while remaining flexible and *** in the industrial sector benefit from the dispersed or decentralized,and efficient environment of ***,we described an SDN method to improve the durability,stability,and load balancing of cloud *** efficacy of our SDN and BC-based implementation was experimentally tested by using various parameters including throughput,packet analysis,response time,bandwidth,and latency analysis,as well as the monitoring of several attacks on the system itself.

关键词： Smart IIoT Blockchain SDN IoT Security Privacy OpenFlow SDN-Controller Data security Cloud computing Cloud management

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