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5th International Conference on Smart electronics and Communication, ICOSEC 2024

作者： Kumar, Kotyada Mohan Kiran Ullas, Karthik Reddy, Vivek Srikar Snehitha, Munaga Sai Malkhed, Mahadevi E. Kumar, K. Dinesh Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India Amrita School of Engineering Amrita Vishwa Vidyapeetham Dept. of Electronics and Computer Engineering Bengaluru India

ISBN: (纸本)9798331504403

With the dawn of the new age of communication, i.e., social media, the world has come into the palms of every individual at every corner of the world. However, easier access to messaging implies easier forms of abuse. As the usage of social media increases, the other side of communication, bullying, also increases. Cyberbullying refers to the concept of harassing individuals or groups through means of the internet with the help of messages or multimedia. Cyberbullying has a huge impact on mental health and research is ongoing in this area to accurately detect bullying text. Existing models are embedded into social media platforms to either ban or apply sanctions on the users involved in the passage of such materials. The proposed study aims to develop a model with a similar approach to detect cyberbullying in comment text. Also, sentiment analysis is performed on the comment text to get an idea of the message's context, thus improving the chances of bullying detection. The work is divided into two stages. In the first stage, the text and the sentiment score are directly passed to the machine learning models. Later, during the second stage word embedding using Word2Vec, a natural language processing technique, is performed on the comment text to obtain vectorized features which are then given to the machine learning models along with sentiment features. It was observed that the latter model shows high levels of success and that it can be deployed in real-world applications. © 2024 IEEE.

关键词： Adversarial machine learning

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A Comprehensive Key Features Analysis and Recommendations based Cyber Intrusion Detection for Satellite-Terrestrial Networks 6

A Comprehensive Key Features Analysis and Recommendations ba...

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6th IEEE Novel Intelligent and Leading Emerging Sciences Conference, NILES 2024

作者： Shehab, Esraa Elsaid, Shaimaa Ahmed Mattar, Ahmed M. Dept. of Electrical Engineering Cairo Egypt Zagazig University Dept. of Electronics and Communications Engineering Zagazig Egypt Military Technical College Dept. of Computer Engineering and Artificial Intelligence Cairo Egypt

ISBN: (纸本)9798350378511

The integration of Satellite-Terrestrial Networks (ISTN) necessitates advanced security measures, particularly Intrusion Detection Systems (IDSs). This study introduces hybrid sequential intrusion detection models for ISTNs, combining Deep Learning (DL) and Machine Learning (ML) techniques. The models employ both anomaly-based and signature-based detection to enhance accuracy, utilizing methods such as Extra Trees (ET), Decision Trees (DT), Random Forest (RF), XGBoost (XGB), and Gated Recurrent Units (GRU). These models are chosen for their superior performance and are used sequentially to improve IDSs effectiveness. RF-based Sequential Feature Selection (RF-SFS) is also utilized to reduce dataset dimensionality, which in turn decreases the computational costs for each model. Evaluations using UNSW-NB15 and STIN datasets-representing terrestrial and satellite traffic, respectively-demonstrate the models' superiority over traditional IDSs. The XGB-ET model achieved 99.99% accuracy in anomaly detection, while the XGB-GRU model attained 89% accuracy in signature-based detection on the UNSW-NB15 dataset. On the STIN dataset, the ET-DT-GRU model reached 96.47% accuracy in signature-based detection. Additionally, RF-SFS reduced execution times, with training and testing speedups up to 2.8x. © 2024 IEEE.

关键词： Network intrusion

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Sensitive Content Detection in Social Networks Using Deep Learning Models and Explainability Techniques 9

Sensitive Content Detection in Social Networks Using Deep Le...

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9th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2024

作者： Perikos, Isidoros University of Patras Computer Engineering and Informatics Department Patras Greece University of Peloponnese Computer Technology Institute and Press 'Diophantus' Electrical and Computer Engineering Dept. Patras Greece

ISBN: (纸本)9798350394191

In the dynamic landscape of online social networks, recognizing sensitive content is essential for safeguarding user privacy, fostering inclusivity, and enhancing diversity awareness. Building on prior research, this study explores new dimensions and methodologies for detecting sensitive content. We examine the temporal evolution of sensitive content, revealing how patterns shift over time, and address cross-linguistic challenges, emphasizing cultural and contextual nuances in detection. We employ advanced machine learning techniques, including deep learning models and BERT that improve the accuracy and robustness of the detection procedure. In the experimental study, BERT transformer reported the best performance in detecting sensitive content in text. Additionally, we incorporate explainability techniques such as LIME and SHAP to provide deeper insights into the model's decision-making processes, ensuring predictions are interpretable and reliable. Our work enhances the theoretical framework of sensitive content detection in social networks and provide methods that are accurate and scalable and can facilitate the creation of user-centric interaction that prioritize privacy and user experience. © 2024 IEEE.

关键词： Adversarial machine learning

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Revolutionizing Diabetic Retinopathy Recognition with Advanced Deep Learning Techniques 1

Revolutionizing Diabetic Retinopathy Recognition with Advanc...

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1st International Conference on Computing, Sciences and Communications, ICCSC 2024

作者： Rajalingam, S. Kanagamalliga, S. Bhavani, U. Chouksey, Mausam Saveetha Engineering College Dept. of Electrical and Electronics Engineering Chennai India Saveetha Engineering College Dept. of Electronics and Communication Engineering Chennai India School of Computer Science University of Petroleum and Energy Studies Dehradun India

ISBN: (纸本)9798350353648

Diabetic retinopathy (DR) is a severe complications arising from diabetes and the primary cause of vision impairment worldwide. Early recognition is essential for efficient intervention and prevention of permanent vision loss. This research focuses on using Deep Learning Techniques (DLT) for accurate and efficient recognition of DR. It utilizes a dataset of retinal images from diabetes, covering several stages of DR progression. Convolutional Neural Networks (CNNs), is DLT subset, are employed for extracting features and performing classification. The proposed model effectively detects subtle retinal abnormalities indicative of DR. This research contributes to existing methods by introducing a novel deep learning approach that shows high specificity and sensitivity in DR detection. The DR-Color-Fundus-Images recognition using DLT were analyzed using available Color-Fundus-Retina datasets. Performance metrics, comprising the area beneath the curve receiver operating characteristic, specificity, and sensitivity, validate the model's accuracy. The outcomes have implications for developing automated screening tools, enabling timely intervention and improving healthcare outcomes for individuals with diabetic retinopathy. This research represents a significant advancement in harnessing deep learning for enhancing DR detection and highlights the importance of technological progress in ophthalmic healthcare. © 2024 IEEE.

关键词： Convolutional neural networks

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Design and Implementation of Cloud Computing Using the NDLC Method Combined with Tunnel Link Split

Design and Implementation of Cloud Computing Using the NDLC ...

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2023 International Conference on Information Technology and Computing, ICITCOM 2023

作者： Wibowo, Iqbal Tri Kurniawan, Asep Noviandri Sulaiman, Nur Fauzi Oktivasari, Prihatin Politeknik Negeri Jakarta Dept. of Informatics and Computer Engineering Depok Indonesia

ISBN: (纸本)9798350359633

Today's education system increasingly incorporates information technology, such as the use of computers, the internet, and mobile devices. Utilization of this information technology can facilitate the teaching and learning process and increase the effectiveness, efficiency and productivity of users, both teachers, students and school administrators. However, given the growing number of users and the amount of data processed, school infrastructure often cannot meet these needs. The system development method will use the NDLC (Network Development Life Cycle) method which will be combined with the Tunnel Link Split to become a system that can be used remotely (remoting). The results of this research are that the results of designing and implementing cloud computing with the NDLC method combined with tunneling can be successful and used. Implementation is also carried out with additional monitoring using observium software and for access to the cloud using additional software, namely owncloud by obtaining an extended processor observium value of 99.90% (idle), detailed observium memory 69% (% of ram), swap storage observium 218.48kb/ s, observium ping response 86.4m/s, and observium traffic port wlo15.63m. © 2023 IEEE.

关键词： Life cycle

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Analysis of *** Email Domain Security Status Using DMARC (Domain-Based Message Authentication, Reporting, and Conformance) 6

Analysis of *** Email Domain Security Status Using DMARC (Do...

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6th International Conference on computer and informatics engineering, IC2IE 2023

作者： Arnaldy, Defiana Theyser, Regina Patricia Politeknik Negeri Jakarta Dept. of Informatics and Computer Engineering Depok Indonesia

ISBN: (纸本)9798350345162

The rapid development of technology not only provides benefits that facilitate all human activities. However, it also encourages more security loopholes due to a person's lack of awareness in maintaining data security. Phishing emails are one of the fastest growing security crimes and harm both businesses and individuals. This research aims to implement DMARC (Domain-based Message Authentication, Reporting, and Conformance) as an email authentication method that can monitor the security status of all types of messages that enter *** email domains. This research uses email phishing and spoofing as penetration testing techniques to analyze how good that email security method is. DMARC is implemented in three types of scenarios (none, quarantine, and reject) which manage to get different delivery rates and the success rate of DMARC work performance reaches 90% in monitoring and managing all email messages that go to the *** email domain. In the situation without policy enforcement, the DMARC system effectively observed a 100.0% external delivery rate during the phishing email delivery test. In the scenario where a quarantine policy was implemented, the DMARC system successfully monitored the external sending of phishing emails at a rate of 88.5%. Similarly, in the case of the reject policy scenario, DMARC effectively oversaw incoming phishing emails with an external delivery rate of 97.7%. © 2023 IEEE.

关键词： Apilogy.id DMARC Email Security Phishing Spoofing

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scGraPhT: Merging Transformers and Graph Neural Networks for Single-Cell Annotation

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IEEE Transactions on Signal and Information Processing over Networks 2025年 11卷 505-519页

作者： Koc, Emirhan Kulkul, Emre Kaynar, Gulara Cukur, Tolga Acar, Murat Koc, Aykut Bilkent University Dept. of Electrical and Electronics Engineering Ankara06800 Turkey UMRAM National Magnetic Resonance Research Center Ankara06800 Turkey Koç University Dept. of Computer Engineering Istanbul34450 Turkey Koç University Dept. of Basic Medical Sciences School of Medicine Istanbul34450 Turkey

The invention of single-cell RNA sequencing (scRNA-seq) has enabled transcriptomic examination of cells on an individual basis, uncovering cell-to-cell phenotypic heterogeneity within isogenic cell populations and tissues. Inevitably, cell type annotation has emerged as a fundamental, albeit challenging task in scRNA-seq data analysis, which involves identifying and characterizing cells based on their unique molecular profiles. Recently, deep learning techniques with their data-driven priors have shown significant promise in this task. On the one hand, task-agnostic transformer networks pre-trained on large-scale biological databases can capture generalizable data representations to serve as foundation models despite their ineffectiveness in characterizing intricate relationships between biological entities such as cells or genes. Contrarily, task-specific graph neural networks (GNNs) can be trained on target datasets to sensitively characterize entity relationships, but they can suffer from relatively poor generalizability. Furthermore, existing GNNs focus exclusively on either homogeneous or heterogeneous relationships, limiting their ability to offer a complete picture of the diverse inner structure of cells. In this study, we propose a novel merged transformer-graph model, scGraPhT, that integrates a pre-trained transformer to extract rich representations of scRNA-seq data with a multi-layered GNN to capture cell-cell, cell-gene, and gene-gene relationships. Different from previous GNNs, scGraPhT examines both homogeneous and heterogeneous relationships through subgraph layers to offer a more comprehensive assessment. Since the graph construction in scGraPhT relies on representations from a pre-trained transformer model, our approach does not require costly training procedures. Moreover, scGraPhT can also be adapted to leverage any transformer-based single-cell annotation method, such as scGPT or scBERT, that produces suitable embedding representations as input

关键词： Tissue culture

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A Review on Bioinformatics using DNA Cryptography 8

A Review on Bioinformatics using DNA Cryptography

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8th International Conference on electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Kairi, Animesh Bhadra, Tapas Karmakar, Samit Verma, Aman Saha, Sonia Gupta, Soumili Chowdhury, Souraja Aliah University Computer Science Kolkata India University of Engineering & Management Dept. of Electronics & Communication Engineering Institute of Engineering & Management Kolkata India

ISBN: (纸本)9798331520762

DNA-based encryption with bioinformatics has emerged as a promising field for secure data storage and transfer. This study analyzes the bioinformatics aspects of DNA cryptography, focusing on important advancements and challenges. We look at techniques like DNA origami, enzymatic reactions, and DNA strand displacement that use DNA sequences to encode and decode data. Additionally discussed are the security implications of DNA-based cryptography, including privacy issues, data integrity issues, and the potential for DNA-based attacks. Finally, we outline some possible directions for further review, such as developing more robust error-correction systems, looking at cutting-edge DNA-based encryption methods using bioinformatics, and addressing the ethical and societal implications of this emerging technology. © 2025 IEEE.

关键词： Bioinformatics

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Enhancing WSNs Lifetime Through GOA based Clustering and Routing 1

Enhancing WSNs Lifetime Through GOA based Clustering and Rou...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Kaushik, Neeraj Singhal, Amit Pandey, Ashish Monad University Dept. of Computer Science and Engineering UP Hapur India GCET Dept. of Electronics and Communication Engineering UP Greater Noida India

ISBN: (纸本)9798350356816

WSNs are regularly battery-powered and it is not continually feasible to recharge or replace them due to a variation of applications. Hence, in this work, a Grasshopper Optimization Algorithm based on routing and power restriction optimization has been proposed for improving the battery lifetime in wireless sensor network. The proposed approach aims to enhance the effective longevity of individual sensor nodes within the network by accomplishing energy-efficient clustering along with energy-aware routing. The outcomes of the suggested method are compared with the several existing optimization methods in WSNs. It is observed that the proposed Grasshopper Optimization Algorithm (GOA) based technique outperforms all the other existing methods in terms of lifetime achieved under various power constraints. For checking robustness of approach, two different locations of base station and various populations of sensing nodes and gateways are also considered. © 2024 IEEE.

关键词： Sensor nodes

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Solar Energy based Smart Home Monitoring and Automation using IOT 4

Solar Energy based Smart Home Monitoring and Automation usin...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Dharmaraj, N. Adithyaa, A. Satyanarayanaa, H. Mouleesh, Y. Rupesh, S. Priyadharshini, K. Rajalakshmi Institute of Technology Dept. of Electronics and Communication Engineering Chennai India Rajalakshmi Institute of Technology Dept. of Computer and Communication Engineering Chennai India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508845

In the pursuit of sustainable living and energy efficiency, integrating renewable energy sources with smart home technologies has become increasingly important. This paper presents a novel system that utilizes solar energy as the primary power source for a smart home, combined with Internet of Things (IoT) technology for monitoring and automation. The primary goal of this project is to reduce waste in conventional ways by making extensive use of energy recovery sources, enhancing safety, and removing environmental impact. Optimizing the equipment's use of program energy in accordance with the actual time of solar power generation is one of the key goals. Another is enhancing the effectiveness of energy transfer through remote monitoring and control using data and smartphone applications. The integration of Internet of Things (IOT) technology with solar energy systems is critical to the sustainability, efficiency, and administration of residential surroundings. This article investigates the deployment capabilities of the Internet of Things (IOT) for solar household energy system automation and monitoring. Future house automation will rely on solar energy and Internet of Things monitoring technologies once this work is completed. © 2024 IEEE.

关键词： Smart homes

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