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1st International Conference on Healthcare Innovations, Software and engineering Technologies, HISET 2024

作者： Gaikwad, Mayuri Diliprao Dutonde, Abhimanyu Tulsiramji Gaikwad-Patil College of Engineering & Technology Departement of Computer Science and Engineering Nagpur India

ISBN: (纸本)9798350360707

The science of digital forensics is crucial in detecting and pursuing criminals since criminal behaviour depends more and more on digital technology. A model for behaviour detection is provided in this paper based on research into the most recent work in the field. when measured against models used in cutting-edge research. This study will help authorities working to prevent cybercrime and cybersecurity identify profiles with specific behaviours. The research focuses on identifying illegal behaviour from social media by evaluating the leading-edge work and suggesting an improved methodology. To get a better-trained model for this study, first collect the "Tweets Dataset for Detection of Cyber-Trolls"dataset from Kaggle. After that perform the preprocessing data for word cloud. the implementation work is complete on the python programming language and jupyer notebook. The results showed that the proposed LSTM model, achieved high accuracy. The suggested model outperformed with a 1.00% accuracy rate. This study will be very helpful to organization that deal with cybercrime and cyber security in identifying and eliminating profiles that exhibit specific behaviours. © 2024 IEEE.

关键词： Tweets

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Regression analysis of squeezing-induced hybrid nanofluid flow in Darcy-Forchheimer porous medium

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Applied Mathematics and Mechanics(English Edition) 2025年第1期46卷 193-208页

作者： K.MUHAMMAD M.SARFRAZ Department of Humanities and Sciences School of Electrical Engineering and Computer ScienceNational University of Sciences and TechnologyIslamabad 44000Pakistan Department of Mathematics Quaid-i-Azam UniversityIslamabad 44000Pakistan

This article presents a mathematical model addressing a scenario involving a hybrid nanofluid flow between two infinite parallel *** plate remains stationary,while the other moves downward at a squeezing *** space between these plates contains a Darcy-Forchheimer porous medium.A mixture of water-based fluid with gold(Au)and silicon dioxide(Si O2)nanoparticles is *** contrast to the conventional Fourier's heat flux equation,this study employs the Cattaneo-Christov heat flux equation.A uniform magnetic field is applied perpendicular to the flow direction,invoking magnetohydrodynamic(MHD)***,the model accounts for Joule heating,which is the heat generated when an electric current passes through the *** problem is solved via NDSolve in *** and statistical analyses are conducted to provide insights into the behavior of the nanomaterials between the parallel plates with respect to the flow,energy transport,and skin *** findings of this study have potential applications in enhancing cooling systems and optimizing thermal management *** is observed that the squeezing motion generates additional pressure gradients within the fluid,which enhances the flow rate but reduces the frictional ***,the fluid is pushed more vigorously between the plates,increasing the flow *** the fluid experiences higher flow rates due to the increased squeezing effect,it spends less time in the region between the *** thermal relaxation,however,abruptly changes the temperature,leading to a decrease in the temperature fluctuations.

关键词： convective boundary condition Darcy-Forchheimer medium hybrid nanofuid Joule heating magnetohydrodynamic(MHD) numerical solution squeezing flow regression analysis

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Centrality of Any Node in Social Network Analysis 2024

Centrality of Any Node in Social Network Analysis

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16th International Conference on Contemporary Computing, IC3 2024

作者： Srivastava, Milli Manju, Manju Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Noida India

ISBN: (纸本)9798400709722

In the contemporary landscape of advanced mobile technology, social media platforms have emerged as prominent arenas for expression, fostering vast volumes of communication ripe for research and analysis. Social network analysis (SNA) stands as a pivotal tool in identifying influential individuals within microblogging platforms, notably Twitter, which serves as a catalyst for online dialogues, trends, and content virality. The applications of discerning influential users on Twitter are multifaceted, ranging from viral marketing and brand analysis to news dissemination, health awareness campaigns, political activism, and governance empowerment. In this study, we introduce a sustainable method, termed Weighted Correlated Influence (WCI), designed to capture the relative impact of both activity on a timeline and characteristics of online users that are trend-specific. Weighted Correlated Influence in social network analysis evaluates the significance of nodes by combining their connectivity and influence, considering both the weight and correlation of their relationships. This metric helps identify key influencers and the overall structure of the network, highlighting nodes with high influence and strong, correlated connections. Our methodology integrates profile activity with underlying network topology to assign influence scores to users, synthesizing their combined effect. To evaluate the efficacy of our proposed methodology, we focus on the Twitter trend #Coronavirus Pandemic and validate the results against another social media trend. Experimental findings demonstrate the superior performance of WCI over existing methods. © 2024 Copyright held by the owner/author(s).

关键词： Tweets

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Security Testing of Android Apps Using Malware Analysis and XGboost Optimized by Adaptive Particle Swarm Optimization

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SN computer science 2024年第1期5卷 92页

作者： Kumar, Pawan Singh, Sukhdip Department of Computer Science and Engineering Deenbandu Chhotu Ram University of Science and Technology Murthal Haryana Sonipat 131039 India

Securing Android apps presents a formidable challenge due to the incessant threat of malicious applications. Traditional solutions have grown less effective in the face of the vast and diverse landscape of these apps, rendering Android smartphones susceptible to novel forms of malware. Recent studies have showcased the efficacy of machine learning techniques in dissecting Android malware. Among these techniques, Extreme Gradient Boosting (XGBoost) has emerged as a standout due to its proficiency in identifying intricate associations among malicious permissions, facilitated by its advantageous parameter combination and selection capabilities. Nevertheless, tuning XGBoost’s hyper-parameters remains a formidable endeavor. To meet this challenge, this paper introduces an innovative security testing approach that synergizes XGBoost with Adaptive Particle Swarm Optimization (APSO) to scrutinize Android app permissions for malware detection. APSO empowers the XGBoost model to optimize its hyper-parameters by meticulously exploring the optimal values within a continuous space. Unlike conventional particle swarm optimization, APSO effectively addresses the concern of getting ensnared in local optimum zones. In this study, a comprehensive experiment is undertaken to forge a dataset (PermiDroid) comprising 10,461 real-world Android apps, comprising 5238 benign and 5223 malicious samples. PermiDroid, alongside two benchmark datasets, is harnessed to measure the performance of the proposed model against various machine learning methods. Four widely regarded evaluation metrics—accuracy, precision, recall, and f1-score—are employed to measure the efficacy of the models. The results unequivocally demonstrate the supremacy of the proposed APSO-XGBoost model in comparison to existing state-of-the-art models concerning security testing. The findings from this research enrich the community by advancing the organization and dissemination of knowledge. © 2023, The Author(s), under exclus

关键词： Adaptive Particle Swarm Optimization Android Ensemble learning Malware analysis Security testing XGBoost

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A Robust Protocol Design to Perform Data Communication over Wireless Sensor Networks with Enhanced Privacy Preserving Principles 4

A Robust Protocol Design to Perform Data Communication over ...

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

作者： Sathyavani, Bandela Kumar Yadav, Rakesh John Shiny, J. Revathi, R. Bulakh, Ashish Kumar Jain, Shilpa School of Engineering Sr University Department of Eee Telangana Warangal India Amity School of Engineering and Technology Amity University Bihar Patna India Velammal College of Engineering and Technology Madurai India Psgr Krishnammal College for Women Department of Computer Science-Information Technology Coimbatore India Cdgi Department of Computer Science and Engineering Indore India Ies College of Technology Department of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798331523923

the growth of wireless sensor networks has transformed all automation and industrial systems. Nonetheless, guaranteeing safe and privacy-preserving connectivity in sensor networks continues to be a difficult task. Current authentication and routing techniques frequently jeopardize privacy, entail significant design intricacy, also vulnerable to malware assaults. To mitigate these restrictions, we present an innovative secure protocol, the Asymmetric Cryptography-based Modified Cuckoo Search Optimization Protocol (MCuSO) for wireless sensor networks. Our proposed technique impacts asymmetric credentials with belief supervision for the privacy preservation of WSN data. This method attains an equilibrium in the middle of security and efficiency. Through comprehensive simulations utilizing the MATLAB platform, we illustrate that MCuSO attains an average network throughput of 94% and a network residual energy of 89%, in comparison to leading other algorithms. Moreover, the protocol efficiently reduces malware attacks. These findings underscore MCuSO as a viable outcome of resource limited WSN devices, providing an advantageous equilibrium of information security, data integrity and data efficacy. © 2025 IEEE.

关键词： Authentication Protocol

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Drone-Based Intelligent System for Social Distancing Compliance Using YOLOv5 and YOLOv6 with Euclidean Distance Metric

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SN computer science 2024年第8期5卷 972页

作者： Parkavi, A. Alex, Sini Anna Pushpalatha, M.N. Shukla, Prashant Kumar Pandey, Ankur Sharma, Sachin Department of Computer Science and Engineering M S Ramaiah Institute of Technology Bangalore India Department of Computer Science and Engineering (AI and ML) M S Ramaiah Institute of Technology Bangalore India Department of Information Science and Engineering M S Ramaiah Institute of Technology Bangalore India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur 522302 India Department of Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur India Manav Rachna International Institute of Research and Studies Faridabad India

The COVID-19 pandemic forced rigorous social distancing measures to be put in place to halt the virus’s spread;compliance was extremely difficult. In response, we have designed a highly advanced intelligent drone system that maintains and controls distance measures following social distance principles through advanced machine learning skills. This paper describes an intelligent monitoring and compliance system in public places using drone technology with a focus on social distance requirements. Boosting the efficiency of human detection in real-time video streams acquired by drones, our solution employs the You Only Look Once version 5 (YOLOv5) and YOLO version 6 (YOLOv6) object detection algorithms which are the most potent computer vision tools today. The distances between the recognized individuals are computed based on the Euclidean distance measurement to distinguish cases of nonadherence to social distance measures. Our technique integrates the recently developed deep learning models with aerial imagery and monitoring to make it easily scalable for population health surveillance. Thus, we prove impressively that the underlying inventive method works well in different environmental conditions and can potentially contribute to people’s safety in the event of a pandemic. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19 Drones Euclidean distance metric Social distancing YOLOv6

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Efficient Deep Learning Models for Toxic Comments Identification and Classification Using LSTM Network 12th

Efficient Deep Learning Models for Toxic Comments Identifica...

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Tiwari, Nandini Rizvi, Sheenu Arora, Deepak Tiwari, Shivam Department of Computer Science and Engineering Amity School of Engineering and Technology Lucknow Amity University Uttar Pradesh Noida India Department of Computer Science and Engineering Sharda University Greater Noida India

ISBN: (纸本)9789819789450

An effective strategy for identifying and categorizing toxic comments is urgently needed due to the overwhelming number of such comments on various internet platforms. While previous research has addressed various forms of toxic behaviour, existing approaches often fall short. Our research fills this need by suggesting a novel method that combines ML with natural language processing (NLP). Building on previous work, we focus on recurrent neural networks (RNNs), specifically LSTM networks, renowned for their adeptness in capturing sequential patterns and contextual information within text. Also, we incorporate dropout layers to enhance model generalization and combat overfitting. Evaluating our models on a benchmark dataset reveals promising outcomes, with the LSTM model achieving an accuracy score of 0.9943 and a loss of 0.0493, while the LSTM model with dropout layers closely follows with an accuracy score of 0.9939 and a loss of 0.0493. These findings underscore how effectively our methodology detects toxic comments, thereby advancing automated moderation systems. Looking ahead, we aim to refine our approach and explore avenues for real-world implementation, aiming to foster healthier online discourse and enhance user safety. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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An Improved Intrusion Detection system for securing academic information on the cloud using a blockchain-based security framework 2

An Improved Intrusion Detection system for securing academic...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Dubey, Krishna Bihari Goyal, Mukta Jaypee Institute of Information Technology Department of Computer Science & Engineering and Information Technology Noida India

ISBN: (纸本)9798350371055

The concept of cloud computing has hastily grown in reputation, allowing businesses to shop and get access to facts from remote servers. Higher educational establishments have additionally adopted cloud services for storing and dealing with academic statistics. But this additionally poses a capability chance to the security and privacy of touchy facts. To deal with those challenges, we advise an advanced intrusion detection system (IDS) to secure the cloud's academic data. This ID uses a blockchain-based total safety framework to ensure the safety and integrity of stored statistics. The proposed IDS utilizes community visitors' monitoring and analysis to stumble upon anomalies and potential protection breaches. It also employs machine-learning techniques like anomaly detection algorithms to identify suspicious activities deviating from everyday behaviour styles. In the event of a detected intrusion, the device triggers an alert to the responsible parties and makes essential movements to mitigate the assault. The combination of blockchain technology offers an additional layer of security by developing an immutable and decentralized ledger to report all data transactions. This guarantees the integrity and authenticity of the academic statistics stored on the cloud. In case of any modifications or changes to the records, the blockchain will record them, creating a transparent audit path for accountability. Furthermore, the proposed IDS also consists of a belief management gadget that evaluates cloud carrier providers' popularity and protection posture. © 2024 IEEE.

关键词： Authenticity Cloud Community Decentralized Detected

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DevConnect: Knowledge Sharing Social Platform

DevConnect: Knowledge Sharing Social Platform

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2024 International Conference on Computational Intelligence and Computing Applications, ICCICA 2024

作者： Jindal, Mayank Tomer, Rahul Dhingra, Deepti Gandhi, Anju Mahajan, Aakanksha Panipat Institute Of Engineering And Technology Department Of Computer Science And Technology Panipat India Panipat India

ISBN: (纸本)9798350306446

"DevConnect"aims to create an interactive platform tailored for developers to collaborate, seek assistance, and discover new projects seamlessly. Through its innovative features, developers can engage in meaningful collaborations, exchange messages for help queries, and explore fresh project ideas effortlessly. At the heart of the platform lies its ability to facilitate collaboration among developers, a vibrant community where knowledge sharing thrives. Users can reach out to fellow developers for assistance, feedback, or guidance, enriching their development journey with valuable insights and support. Central to the platform{\prime}s functionality is its robust search feature, enabling developers to explore a diverse range of projects based on their interests, skills, and preferences. Whether seeking inspiration for a new endeavour or searching for specific project types, developers can easily navigate the platform to discover exciting opportunities. Furthermore, developers have the option to follow their favourite peers, allowing them to stay updated on their latest projects, contributions, and insights. This feature not only cultivates a sense of camaraderie within the community but also empowers developers to build meaningful connections and expand their networks. The Social Media Showcase project is designed with user-friendliness in mind, ensuring that developers can effortlessly navigate the platform and access its wealth of resources. With its intuitive interface and seamless functionality, developers can focus their energy on what matters most: innovation and collaboration. By providing a dynamic and interactive space for developers to connect, share, and discover, the Social Media Showcase project seeks to enrich the development community and inspire creativity in the ever-evolving landscape of technology. © 2024 IEEE.

关键词： Social networking (online)

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A study on Retinopathy of Prematurity(ROP) Screening Using Deep Learning Approaches and a Blood Vessel Segmentation Framework for ROP Affected Eyes 2

A study on Retinopathy of Prematurity(ROP) Screening Using D...

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2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Madhu, K. John, Shinu Mathew Joseph, Asha Abraham, Bejoy Apj Abdul Kalam Technological University Computer Science and Engineering Thiruvananthapuram India St.Thomas College of Engineering and Technology Mattannur Department of Computer Science and Engineering Mattannur India Amal Jyothi College of Engineering Department of Information Technology Kottayam India College of Engineering Muttathara Department of Computer Science and Engineering Thiruvananthapuram India

ISBN: (纸本)9798350328202

The retina contains millions of photoreceptive cells called cones, rods, and blood vessels that nourish these cells. Retinopathy of prematurity is an eye disease affecting the retina of a prematurely born infant and is one of the leading causes of childhood blindness at a global level. Pediatric fundus images captured using retina imaging devices such as Retcams have been used by doctors for analyzing the retina and detecting various abnormalities present in the retina. Multiple stages in a single ROP image, underdeveloped retina vessels, immaturity of the macula and fovea,image quality issues like illuminations and artifacts present in an image make ROP disease detection, classification, and segmentation a difficult task for both an experienced practitioner and a computer-aided diagnostic system(CAD). Nowadays automated image analysis using artificial intelligence techniques is gaining popularity among practitioners and researchers. The objective of this survey is to do a systematic review of published works that use deep learning approaches for disease detection, classification, and segmentation in the context of Retinopathy of prematurity (ROP). This work presents a performance analysis and comparison of state-of-the-art deep learning architectures us ed for the above tasks. We also provide potential avenues for future research in ROP detection, classification, and segmentation using deep learning. After the study we proposed a segmentation framework called ROP-seg using a publicly available dataset for blood vessel segmentation that uses the combined power of Unet and Efficientnet architectures. The proposed system shows promising results in blood vessel segmentation of ROP affected eyes. © 2024 IEEE.

关键词： Blood vessels

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