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

作者： Janet Kumari, J Jesy Thangam, S. Ghosh, Archishman Kolhe, Dhruv Vijay Ghosh, Soumish Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331523923

As cities expand, vehicles and congestion become more complex. Efficient vehicle-to-vehicle contact networks are needed for road safety and efficient traffic flow. Thus, Vehicular Ad Hoc Networks are needed to overcome all the issues. The Adhoc On-Demand Distance Vector (AODV) routing protocol is used in VANETs. However, this protocol has many challenges in urban settings due to high vehicle density, rapid topology changes, and non-line-of-sight conditions. This work improves AODV by integrating Quality of Service (QoS) metrics. The prioritizing factors such as overhead and end-to-end delay are used to create a more efficient protocol for urban traffic. The SUMO simulator is used for realistic traffic patterns and NS-2 for network analysis. An evaluation of the protocol against the classical AODV is done and important metrics for real-time communication are focused. The enhanced AODV showed marked improvements. It achieved lower overhead in route finding, an increase in the number of packets sent, and lower end-to-end delay. © 2025 IEEE.

关键词： Vehicular ad hoc networks

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Dynamic Resource Provisioning for 5G Networks: Comparative Analysis of Various Fuzzy Logic Based Approaches for Efficient Resource Allocation

Dynamic Resource Provisioning for 5G Networks: Comparative A...

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2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Chennupalle, Divya Mahitha, Desham Nithin, G. Mohan, Penumarty Krishna Supriya, M. Panwar, Reena Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331521394

The applications such as online gaming, video chats, and emergency services demand the efficient deployment of resources in 5G networks in a dynamic fashion. Conventional approaches frequently fail to handle fluctuating workloads, which leads to reduced service quality and underuse of resources. To solve this, this paper proposes a hybrid resource allocation approach that combines fuzzy logic with machine learning (ML) models. This work uses Type-1 fuzzy systems, and Neuro-fuzzy systems with regression to understand real-time characteristics for optimal provisioning. Prediction accuracy is improved via machine learning models, such as Linear Regression (LIR), Random Forest Regression (RFR), Support Vector Regression (SVR), Logistic Regression (LOR) and Gradient Boosting Regression (GBR);performance is measured using metrics like Mean Squared Error (MSE), Mean Absolute Error (MAE) and R2 Score. Among these, the Random Forest Regression model achieves the best results, with the lowest Mean Squared Error of 11.601, Mean Absolute Error of 1.326, and an R2 value of 0.867. Through the integration of fuzzy systems and machine learning models, the technique provides a scalable and adaptive solution to the intricacies of 5G networks, guaranteeing dependable, efficient, and flexible resource management across a range of application requirements. © 2025 IEEE.

关键词： Resource allocation

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SmartEagleEye:A Cloud-Oriented Webshell Detection System Based on Dynamic Gray-Box and Deep Learning

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Tsinghua science and Technology 2024年第3期29卷 766-783页

作者： Xin Liu Yingli Zhang Qingchen Yu Jiajun Min Jun Shen Rui Zhou Qingguo Zhou School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China College of Computer Science and Technology Zhejiang UniversityHangzhou 310058China School of Computing and Information Technology University of WollongongWollongong 2500Australia

Compared with traditional environments,the cloud environment exposes online services to additional vulnerabilities and threats of cyber attacks,and the cyber security of cloud platforms is becoming increasingly prominent.A piece of code,known as a Webshell,is usually uploaded to the target servers to achieve multiple *** Webshell attacks has become a hot spot in current ***,the traditional Webshell detectors are not built for the cloud,making it highly difficult to play a defensive role in the cloud ***,a Webshell detection system based on deep learning that is successfully applied in various scenarios,is proposed in this *** system contains two important components:gray-box and neural network *** gray-box analyzer defines a series of rules and algorithms for extracting static and dynamic behaviors from the code to make the decision *** neural network analyzer transforms suspicious code into Operation Code(OPCODE)sequences,turning the detection task into a classification *** experiment results show that SmartEagleEye achieves an encouraging high detection rate and an acceptable false-positive rate,which indicate its capability to provide good protection for the cloud environment.

关键词： Webshell detection cloud web security deep learning

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Exploring Ensemble Architectures for Lung X-Ray Multi-Class Image Classification using CNN-LSTM 3

Exploring Ensemble Architectures for Lung X-Ray Multi-Class ...

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3rd International Conference on Machine Learning and Data engineering, ICMLDE 2024

作者： Nair, Rekha R Singh, Tripty Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwavidyapeetham Bengaluru India

Early diagnose of lung diseases is important as it aids In the treatment of the diseases and increment of the patient's quality of life. Chest X-rays are a widely used and cost-effective diagnostic tool for examining the lungs and detecting abnormalities. However, interpreting chest X-ray images can be challenging, even for experienced radiologists, due to the complexity of lung anatomies and the subtle variations in disease manifestations. Thus, the ensemble-based approach to Lung X-Ray Multi-Class image classification has been proposed and used Convolutional Neural Network and Long Short-term Memory (CNN-LSTM). The proposed model is compared with Several models such as ensemble of DenseNet and InceptionV3, Vgg16 and Mobilenet V2 and deep bidirectional LSTM(DBLSTM). The proposed model applied for multiclass classification on lung disorders such as TB, normal, pneumothorax, pneumonia, COVID-19, and cardiomegaly. The proposed method provides a testing F1-Score of 94%, Recall of 99%, Precision of 95% and accuracy of 89.31% compared to other ensemble models. The proposed model can be utilized for medicos as a second opinion for diagnosis and treatment planning. © 2025 The Authors. Published by Elsevier B.V.

关键词： Pulmonary diseases

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An EETR Approach for Therapeutic Response Prediction Using Gene Expression and Drug Properties 3rd

An EETR Approach for Therapeutic Response Prediction Using G...

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3rd International Conference on computing and Communication Networks, ICCCN 2023

作者： Selvi Rajendran, P. Jawahar, Janiel Computer Science and Engineering School of Computing Hindustan Institute of Technology and Science Chennai India

ISBN: (纸本)9789819726707

In recent years, the role of computational methods such as machine learning and deep learning has evolved to help better understand an individual’s response to drugs. Through advancements in the discipline of precision medicine, cancer therapies are made based on an individual's pharmacogenomics data. In this way, doctors can make informed decisions about patient outcomes and reduce treatment costs. Beyond these advancements, there is enough void to precisely predict drug response. A drug’s response to therapy is dependent on a number of factors such as the drug’s physicochemical properties, pharmacokinetics, metabolism, protein, mutation, somatic variation, environment, and much more. Studying the correlation of more of these factors and their association with the drug is one of the promising ways to bridge the gap in improving results. Extra tree regression is an ensemble-based algorithm and is less prone to overfitting than other ensemble learning algorithms particularly when low bias and reduced overfitting are desired, and computational efficiency is important. In this paper, we have applied the drug’s physicochemical properties to predict the Inhibition values using an enhanced extra tree regression (EETR) algorithm. The EETR algorithm prediction outperformed both the random forest and original ETR algorithm in terms of both MSE and R2 score. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Automatic Sentimental Analysis by Firefly with Levy and Multilayer Perceptron

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computer Systems science & engineering 2023年第9期46卷 2797-2808页

作者： D.Elangovan V.Subedha School of Computing Computer Science and EngineeringSathyabama Institute of Science and TechnologyChennai600118India Computer Science and Engineering Panimalar Institute of TechnologyChennai600069India

The field of sentiment analysis(SA)has grown in tandem with the aid of social networking platforms to exchange opinions and *** people share their views and ideas around the world through social media like Facebook and *** goal of opinion mining,commonly referred to as sentiment analysis,is to categorise and forecast a target’s *** on if they provide a positive or negative perspective on a given topic,text documents or sentences can be *** compared to sentiment analysis,text categorization may appear to be a simple process,but number of challenges have prompted numerous studies in this area.A feature selection-based classification algorithm in conjunction with the firefly with levy and multilayer perceptron(MLP)techniques has been proposed as a way to automate sentiment analysis(SA).In this study,online product reviews can be enhanced by integrating classification and feature *** firefly(FF)algorithm was used to extract features from online product reviews,and a multi-layer perceptron was used to classify sentiment(MLP).The experiment employs two datasets,and the results are assessed using a variety of *** account of these tests,it is possible to conclude that the FFL-MLP algorithm has the better classification performance for Canon(98%accuracy)and iPod(99%accuracy).

关键词： Firefly algorithm feature selection feature extraction multi-layer perceptron automatic sentiment analysis

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Discovering Social Media Influencers Using a Deep Regression Analysis

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SN computer science 2025年第4期6卷 1-18页

作者： Ramya, G.R. Bagavathi Sivakumar, P. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

Highly influential users (IUs) play a vital role in disseminating information on online social networks (OSNs). Recognizing IUs is crucial for brand awareness, strategic marketing and consumer engagement. Researchers have developed the temporal influential model (TIM) and incremental logistic regression (ILLR) to find out the probability of a user being influential on Twitter. However, accurately identifying IUs requires considering multiple user and tweet attributes. This study proposes a model for predicting IUs by analyzing user and trend-based attributes from tweets, calculating a weighted associated influence (WAI) score for each user and enhancing IU prediction through user similarity, influence degree, and trustworthiness metrics. The goal is to combine the TIM with deep regression (TIM-DeepReg) to improve accuracy in predicting high IUs on OSNs while handling large amounts of data and attributes. The TIM-DeepReg model combines convolutional neural network (CNN) with regression to automatically learn user and tweet attributes and enhance IU prediction efficiency. Lastly, the experimental findings show that the TIM-DeepReg model surpasses other models in IU prediction for the Tariff, Service, and Punctuality topics of airline services on Twitter and Facebook, with 87.4%, 88.9%, and 88.8% of the most influential individuals predicted. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep regression Influencer on social networks Influencer prediction from Facebook and Twitter Temporal influential model Weighted associated influence score

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HybridViT: An Approach for Alzheimer’s Disease Classification with ADNI Neuroimaging Data

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SN computer science 2025年第4期6卷 1-18页

作者： Tekumudi, Vivek Sai Surya Chaitanya Ramanathan, Annamalai Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

Alzheimer’s disease (AD) is a prevalent neurological disorder characterized by progressive brain cell degeneration and atrophy, leading to a gradual decline in cognitive and functional abilities. Timely diagnosis is paramount in potentially delaying or preventing the course of this debilitating condition. Magnetic Resonance Imaging presents a non-invasive means for longitudinal monitoring and serves as a crucial biomarker for tracking disease progression. In particular, Structural Magnetic Resonance Imaging (sMRI) enables the quantification of atrophy, which is a reliable indicator for assessing the precise stage and severity of AD-related neurodegeneration. In this research, we employ advanced machine learning techniques to address the challenge of accurate and early diagnosis of AD. We utilize a comprehensive dataset comprising five stages of 2D sMRI Image data, encompassing AD, Cognitively Normal, Mild Cognitive Impairment, Early Mild Cognitive Impairment, and Late Mild Cognitive Impairment classes. To optimize the classification process, we explore a novel approach that combines a Vision Transformer with pre-trained convolutional neural networks. Our study includes binary and multi-class classification tasks, and the performance of 26 pre-trained Keras Deep Learning models is assessed. Notably, The DenseNet121+ViT model achieves 91.26% accuracy in multi-class classification. For binary classification, MobileNetV2+ViT, EfficientNetB4+ViT, and MobileNet+ViT achieve accuracies of 92.33%, 97.82%, and 94.81%, respectively. These results highlight our approach’s potential to improve AD diagnosis accuracy and underscore the importance of deep learning in early detection of neurological disorders. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Alzheimer’s disease (AD) Alzheimer’s disease neuroimaging initiative (ADNI) HybridViT Pre-trained models Vision transformer

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CSG_IACO: an efficient stigmergic-based improved ACO routing strategy to determine the effective path based on traffic density in V2V networks

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International Journal of Information Technology (Singapore) 2025年第3期17卷 1883-1908页

作者： Kumari, J. Jesy Janet Thangam, S. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

Vehicular ad hoc networks (VANETs) are an essential element and building block of the autonomous vehicle system. VANETs, a subcategory of mobile ad hoc networks (MANETs), stand out due to certain predetermined attributes. In VANETs, optimal path routing offers solutions to address issues like traffic density, node mobility, link failure, and bandwidth. Finding the optimal VANET road path between source and destination conditions is difficult. Extensive paths increase network overhead, communication costs, and failure probability, reducing routing efficiency. We present the new traffic density-based stigmergic ant colony optimization routing (CSG_IACO) to improve routing performance. Improved ant colonies stimulate the proposed CSG_IACO, which measures inter-vehicular density. Ant behavior serves as the inspiration for the improved ACO technique, a meta-heuristic methodology. Phase 1 of the CSG_IACO involves using the traffic density to form a cluster among the vehicles. Phase 2 of the suggested approach is Stigmergy-based ant colony optimization, which guides ants to indirectly communicate with each other to act upon the change in the environment to find a best path. This improves protocol efficiency in all aspects. The simulations using NS 2.35 and SUMO indicate that the proposed system considerably reduces obstacles to finding the optimal path compared to existing systems. The simulations show that our suggested scheme performs better than AODV-DATRLD, ACO-DATRLD, and CaCOIoV in terms of performance including the count of delivered packets, throughput, end-to-end delay, the average count of cluster formations per cluster head, and the number of dropped packets. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Ant colony optimization Cluster Optimal path QoS Stigmergy Traffic density VANETs

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Crypto Trading Platform: MERN Stack with Real-Time Data and Social Features 2

Crypto Trading Platform: MERN Stack with Real-Time Data and ...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Gurupriya, M. Mouhitha, A. Gujjula, Saranya Ankireddypalli, Okesh Reddy Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science & Engineering Bengaluru India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science & Engineering Bangalore India

ISBN: (纸本)9798331505745

The increasing complexity of cryptocurrency markets necessitates the development of efficient portfolio management tools that provide real-time tracking, price updates, and market awareness. This paper focuses on an advanced concept of a cryptocurrency wallet service provision that provides a summary of available coins, their prices, available quantities, and customers' transaction histories. Developed for the 'frontend' using *** and for the 'backend' using ***, which uses the CoinGecko API for receiving live market prices and collecting curated news blog articles from other APIs. The data related to the wallets are efficiently managed since the application is built based on the MongoDB schema, and in addition to this, the user authentication is also integrated. A responsive design of the user interface allows for showing both a portfolio summary and the latest news related to cryptocurrencies, thus suggesting an 'everything in one place' solution for users interested in cryptocurrencies. This system shows how real-time data integration could be implemented, how the system is friendly to users, and how the system manages the content. This system could be expanded in the future with predictive analytics and an AI recommendation system for the portfolio management and contents. © 2025 IEEE.

关键词： Financial markets

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