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Tracing Information Flow in Clustered Complex Networks: Unraveling Centrality Patterns with m-PageRank

SN computer science 2025年第3期6卷 1-11页

作者： Cheriyan, Jo Department of Computer Science and Engineering Saintgits College of Engineering Kerala Kottayam 686532 India

Social network analysis (SNA) examines the social structures and relational patterns among entities, which are represented as nodes and edges within a network. It finds extensive application in various fields to derive insight into how these entities interact and exert influence on one another. Clustering refers to the tendency of nodes to form cohesive groups, whereby nodes within the same group exhibit higher levels of interconnection compared to those outside it. Understanding the flow of information through these clusters requires advanced methodologies to account for the intricacies and dynamics inherent in clusters, as their complexity often poses significant challenges. This research addresses the complexities associated with the exchange of information within clusters of real-world networks by analyzing the interactions among individual nodes and the pathways along which information is disseminated within these clusters. The article introduces the novel Awareness-Regulated-Spreading (ARS) model, an epidemic-based framework that elucidates how information propagates across networks by considering varying levels of transmissibility, ranging from localized sources to widespread dissemination within complex networks through activation probability. The experimental analysis reveals that the diffusion rate through a single edge is superior to that through triangular edges, and the activation probability surpasses that of state-of-the-art models such as the LACS and GACS models. As the probability of activation of clusters increases, the diffusion of information within these clusters also intensifies, although the clustering coefficient negatively impacts the diffusion process. In addition, the article explores the dynamics of information diffusion in the context of feedback loops and various edge characteristics. These advanced techniques offer deep insights into the flow of information, thereby facilitating more informed decision making in a highly connected worl

关键词： Activation probability Centrality Clusters Complex networks Information diffusion

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Hate Speech Detection in Social Media Using BiLSTM 11

Hate Speech Detection in Social Media Using BiLSTM

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11th IEEE International Conference on Advances in Computing and Communications, ICACC 2024

作者： Kuruvilla, Sheen Maria Fathima, Shifa Suryalekshmi, S. Saju, Varsha Monica Pattani, Rose V Saintgits College of Engineering Computer Science & Engineering Kottayam India

ISBN: (纸本)9798350379136

Social media platforms also function as breeding grounds for hate speech. Hate speech is offensive language targeting people based on their characteristics, including gender, race, or religion. The existing hate speech detection system relies on sentiment analysis, which is limited to processing static data that has been extracted from social media platforms beforehand. This research focuses on the development of a hate speech detection system specifically designed for social media platforms. By utilizing Bidirectional Long Short-Term Memory (BiLSTM) networks, the proposed system seeks to precisely detect and categorize hate speech in real time, allowing for rapid mitigation and intervention measures. The network can grasp both past and future context by processing text inputs bidirectionally, enabling a thorough comprehension of semantic linkages and linguistic nuances. The system integrates a chat evaluator module to monitor real-time conversations, issuing warnings and blocking users based on a threshold of detected hate speech occurrences. The implementation of a bidirectional strategy improves the model's overall accuracy and decreases false positives by enhancing its capacity to identify minute fluctuations in hate speech patterns. This initiative contributes to the continuous endeavors aimed at combating hate speech, thereby fostering a digital environment that is more inclusive and secure. © 2024 IEEE.

关键词： Adversarial machine learning

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Legal Assistance Redefined: Transforming Legal Access with AI-Powered LegalLink

Legal Assistance Redefined: Transforming Legal Access with A...

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2024 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems, SPICES 2024

作者： Jacob, Susan Jacob, Prisly Mary Cheriyan, Jo Ismail, Safad Saintgits College of Engineering Department of Computer Science and Engineering Kerala Kottayam India

ISBN: (纸本)9798350376135

In today's scenario, securing prompt and accurate legal aid remains tough for many due to India's complex legal system, a scarcity of lawyers, high fees, and widespread legal unawareness. To address these issues, we plan to create a comprehensive chatbot designed to provide easy access to valuable legal information for the general public. The LegalLink chatbot leverages the Nomic AI text encoder for encoding user queries, followed by the Retrieval-Augmented Generation (RAG) model for retrieving and integrating external knowledge sources into prompt templates. Additionally, it utilizes the Mixtral-8x7B-Instruct-v0.1 model for generating contextually rich and accurate responses tailored to legal inquiries. This paper outlines the chatbot's architecture, various ML models used, and the methodology adopted. Leveraging cutting-edge artificial intelligence, LegalLink revolutionizes the way people access legal guidance, offering timely, accurate, and user-friendly assistance. © 2024 IEEE.

关键词： Natural language processing systems

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An Integrated Blockchain Framework for Secure Data Sharing in IoT Fog Computing

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清华大学学报自然科学版（英文版） 2025年第3期30卷 957-977页

作者： Peda Narayana Bathula M.Sreenivasulu Department of Computer Science Engineering Jawaharlal Nehru Technological UniversityAnanthapur 515002India Department of Computer Science Engineering Matrusri Engineering CollegeHyderabad 500059India

The importance of secure data sharing in fog computing is increasing due to the growing number of Internet of Things(IoT)*** article addresses the privacy and security issues brought up by data sharing in the context of IoT fog *** suggested framework,called"BlocFogSec",secures key management and data sharing through blockchain consensus and smart *** existing solutions,BlocFogSec utilizes two types of smart contracts for secure key exchange and data sharing,while employing a consensus protocol to validate transactions and maintain blockchain *** process and store data effectively at the network edge,the framework makes use of fog computing,notably reducing latency and raising *** successfully blocks unauthorized access and data breaches by restricting transactions to authorized *** addition,the framework uses a consensus protocol to validate and add transactions to the blockchain,guaranteeing data accuracy and *** compare BlocFogSec's performance to that of other models,a number of simulations are *** simulation results indicate that BlocFogSec consistently outperforms existing models,such as Security Services for Fog Computing(SSFC)and Blockchain-based Key Management Scheme(BKMS),in terms of throughput(up to 5135 bytes per second),latency(as low as 7 ms),and resource utilization(70％to 92％).The evaluation also takes into account attack defending accuracy(up to 100％),precision(up to 100％),and recall(up to 99.6％),demonstrating BlocFogSec's effectiveness in identifying and preventing potential attacks.

关键词： BlocFogSec Internet of Things(IoT) fog computing blockchain

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VisualBlend - A Mixed Approach on Forensic Image Generation and Recognition 6

VisualBlend - A Mixed Approach on Forensic Image Generation ...

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6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Unnikrishna, Pillai Midhun Pulkuzhiyil, Vinayak Krishna, Jyothika Wilson, Naveen Kumar, Veena A. Saintgits College Of Engineering Dept. of Computer Science and Engineering Kottayam India

ISBN: (纸本)9798350381689

An extensive use case of Generative Adversarial Networks (GANs) that not only combines artistic creativity with technological innovation but also significantly improves identification precision, filling in critical gaps in the smooth integration of textual descriptions with visual representations. The motivation is rooted on the difficulties that currently exist in accurately identifying human face sketches by directly transforming them into images, an area where prior approaches were inadequate. Our special dual-function technology is designed to get beyond these obstacles and provides a fresh approach for forensic investigators, artists, and sculptors. It has the ability to convert text to image as well as sketch to image. This easy transformation of textual and drawing data represents an integrated combination of creativity and technology that is set to have an impact on a number of disciplines, such as suspect identification and crime scene reconstruction. © 2024 IEEE.

关键词： Generative adversarial networks

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Prediction and analysis of Covid-19 using the Deep Learning Models

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Research on Biomedical engineering 2024年第1期40卷 183-197页

作者： Indira, V. Geetha, R. Umarani, Srikanth Priyadarshini, D. Annal Department of Computer Science and Engineering S.A. Engineering College Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India

Purpose: The difficulty of diagnosing several lung disorders, including atelectasis, cardiomegaly, lung cancer, and COVID-19, is a challenging problem and needs to be addressed. These conditions exhibit some symptoms and demand advanced medical imaging process, thorough clinical assessments, and innovative procedures for accurate diagnosis. The shortage of qualified radiologists further makes the problem more complex to deal with. COVID-19 in particular has resulted in a remarkable number of fatalities around the world. Children below the age of 5 and individuals over 65 are more likely to be affected by lung disorders. It is very hard to diagnose and manage COVID-19 absolutely, but it can be identified earlier by employing computer-aided diagnosis (CAD) technologies to make timely diagnosis. Currently, radiologists adopt technologies, which are driven by artificial intelligence. By using them, medical imaging data, such as chest X-rays and CT scans, can be investigated to identify patterns to diagnose the severity of the virus. This expedites the diagnostic process and enhances accuracy, facilitating more timely and precise medical interventions. The efficiency of artificial intelligence in processing large datasets can directly help healthcare professionals in making diagnosis quicker and more accurate. The objective of the work in this paper is to design and implement deep learning model classifiers, which will effectively categorize the patterns found in the X-rays and CT scans. Methods: Three techniques for categorization are exploited to propose an entirely new hybrid convolutional neural network (CNN) model in this context. MRI and CT image categorization in the first classification method employ Fully Connected (FC) layers. The weights are calculated and tuned for training the algorithm over multiple periods to deliver the maximum precision for classification. The most accurate MRI and CT image characteristics are studied, and deep learning model classifiers

关键词： COVID-19

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Design and construction of a portable e-nose system for human exhaled breath VOC analysis

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Materials Today: Proceedings 2022年 58卷 422-427页

作者： Binson, V.A. Akbar, Rizwana Thankachan, Nisha Thomas, Sania Department of Electronics Engineering Saintgits College of Engineering Kerala Kottayam686532 India Department of Computer Science & Engineering Saintgits College of Engineering Kerala Kottayam686532 India

This paper describes the design and fabrication of a system that can analyze the human exhaled breath volatile organic compounds (VOCs). The expelled breath contains several VOCs in addition to nitrogen, oxygen, carbon dioxide, and water vapor. Some of these VOCs are biomarkers for certain lung diseases like lung cancer, cystic fibrosis, chronic obstructive pulmonary disease (COPD), etc, and they can even identify smokers and non-smokers by variations of VOCs. The development of the electronic nose (e-nose) with five metal oxide semiconductor (MOS) gas sensors, control circuit, and processing circuit is explained in this paper. The system is tested and verified in the detection of breath VOCs. The clinical feasibility of the system is checked in a small pilot study involving 4 lung cancer patients, 4 chronic obstructive pulmonary disease patients, 4 asthma patients, and 4 healthy controls including smokers and non-smokers. The results of the study are also included, along with the conclusions drawn from the work. The e-nose system developed with TGS gas sensors is economical and uses a non-invasive method of breath sampling, and surely it will be a diagnostic tool for the detection of lung cancer, COPD, and other major respiratory diseases in near future. © 2022

关键词： Volatile organic compounds

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A Technical Assessment of SIR, SIR-V and SEIR Epidemic Models 3

A Technical Assessment of SIR, SIR-V and SEIR Epidemic Model...

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3rd IEEE International Conference on Recent Advances in Systems science and engineering, RASSE 2023

作者： Cherian, Jacob P Kizhakkethottam, Jubilant J Saintgits College of Engineering Computer Science and Engineering India

ISBN: (纸本)9798350341676

The accurate modeling of epidemic dynamics plays a pivotal role in understanding and managing the spread of infectious diseases. In the wake of the global COVID-19 pandemic, the application of mathematical models to comprehend disease transmission has gained renewed importance. This paper presents a meticulous comparative analysis of three prominent epidemic models-SIR (Susceptible-Infectious-Recovered), SIR (Susceptible-Infectious-Recovered with Vaccination) and SEIR (Susceptible-Exposed-Infectious-Recovered)-in the context of COVID-19. Through a comprehensive examination of these models, this study explores their efficacy in capturing the intricate dynamics of the COVID-19 pandemic. It delves into the underlying assumptions, mathematical formulations, and practical implications of each model. Furthermore, it highlights the suitability of each model based on real-world epidemiological data and the dynamic nature of the pandemic. The analysis encompasses key factors such as varying immunity levels, the role of exposed individuals, and the potential for reinfection. In conclusion, this comparative analysis serves as a guide for researchers, epidemiologists, and policymakers seeking to better understand and forecast the spread of COVID-19 and similar infectious diseases. It sheds light on the nuances of each model's applicability and provides a foundation for informed decision-making in disease control and mitigation strategies. © 2023 IEEE.

关键词： COVID-19

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Probabilistic Feamework for Modeling and Analyzing Complex Networks 15

Probabilistic Feamework for Modeling and Analyzing Complex N...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Nair, Lekshmi S Cheriyan, Jo Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India SAINTGITS College of Engineering Department of Computer Science and Engineering Kottayam India

ISBN: (纸本)9798350370249

Complex networks, characterised by intricate structures arising from the relationships and interactions among their constituent elements, play a pivotal role in various domains such as social networks, biological systems, and technological infrastructures. The evolution and dynamics of complex networks are analysed for various applications such as link prediction, information diffusion, and recommendation systems. Recent research has focused on the study of structural motifs and the probabilistic analysis of motifs to capture the dynamics of the network. The motifs, considered building blocks of the larger network structure, are recurrent patterns in the network. This study focuses on the different probabilistic models built on triads, which are sets of three interconnected nodes. We investigate six different probability distributions, including Poisson, Gamma, Exponential, Power Law, Gaussian, Lorentzian, and Gaussian Mixture Models (GMMs) that can be applied in the complex network to capture the insight of the connectivity pattern. The applications of this research extend to refining network models, improving simulations, and enhancing our understanding of the likelihood of observing distinct patterns within complex social networks. © 2024 IEEE.

关键词： Poisson distribution

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Guardian Alert: A Deep Learning Approach for Driver Drowsiness Detection and Force Sensing Integration 3

Guardian Alert: A Deep Learning Approach for Driver Drowsine...

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3rd IEEE International Conference on Recent Advances in Systems science and engineering, RASSE 2023

作者： Thomas, Jitty Tresa Mathew, Joseph George, Melwin Saji, Meril Rachel Varghese, Renju Rachel Saintgits College of Engineering Computer Science and Engineering Kottayam India

ISBN: (纸本)9798350341676

The proposed project addresses the critical issue of driver drowsiness, which is a major cause of accidents on the road. The system utilizes advanced technology, including video image analysis and pressure sensing, to monitor the driver's state while behind the wheel. By detecting the closure of the driver's eyes and measuring the pressure on the steering wheel, the system can accurately determine whether the driver is becoming drowsy or losing focus. The detection of drowsy eyes is particularly crucial, as the duration of eye closure during drowsiness is longer than normal blinking, making it a clear indicator of potential danger. The integration of the pressure sensing system further enhances the accuracy of detecting drowsiness, as it captures instances of loosening grip on the steering wheel, which may signify a decline in attentiveness. This combination of eye and grip monitoring enables the system to issue timely alerts to the driver, warning them to stay alert and avoid potential accidents. Moreover, the ability to detect the presence of hands on the steering wheel also provides an additional layer of safety, ensuring that deliberate actions or distractions are also accounted for. Overall, this comprehensive drowsy eye detection system is seen to be 90% effective in its accuracy which was obtained using F1 score and holds significant social importance by preventing accidents, while also delivering economic benefits through reduced accident-related costs and enhanced overall productivity on the roads. © 2023 IEEE.

关键词： Accidents

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