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2025 International Conference on computer, Electrical and Communication engineering, ICCECE 2025

作者： Madasanal, Kartik Gudnavar, Anand Nagarmunoli, Sreedevi Madagouda, Basavaraj Naregal, Keerti Visvesaraya Technological University Jain College of Engineering and Research Department of Computer Science and Engineering Belagavi India Graphic Era University Department of Computer Science and Engineering Dehradun India S.G.Balekundri Institute of Technology Visvesaraya Technological University Department of Computer Science and Engineering Belagavi India

ISBN: (纸本)9798331513894

This paper develops a Smart Public Transportation System using RFID technology, IoT integration, and a MERN-based web application for increasing the efficiency, accessibility, and user experience of urban transit systems. It addresses problems such as unreliable bus tracking, lack of support for multilingualism, and ineffective communication between operators and passengers. The key hardware components include RFID tags for bus identification, and IoT-enabled bus stations with RFID scanners, speakers, GSM modules for data transmission, and LCD displays. These ensure accurate real-time updates about the bus locations. On the software side, it is supported by a MERN stack, including MongoDB, ***, ***, and ***, providing a responsive web interface that's complemented by *** for dynamic route visualization and WebSockets for live updates. Multilingual support through i18next makes it inclusive for various user groups. The system is efficient in that it only updates when RFID scanners capture bus tags at certain checkpoints, thereby reducing server load much more than GPS-based systems. Additional features include route tracking, interactive maps, ticket pricing, and anonymous passenger chats. Crowd-sourced feedback mechanisms and an admin panel for bus management ensure the reliability and scalability of the system. This work shows the feasibility of cost-effective, scalable, and user-centric modernization of public transportation systems, paving the way for further enhancements such as integrated payment systems and AI-based route optimization. © 2025 IEEE.

关键词： Liquid crystal displays

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ICON: Improving Inter-Report Consistency in Radiology Report Generation via Lesion-aware Mixup Augmentation

ICON: Improving Inter-Report Consistency in Radiology Report...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Hou, Wenjun Cheng, Yi Xu, Kaishuai Hu, Yan Li, Wenjie Liu, Jiang Department of Computing The Hong Kong Polytechnic University Hong Kong Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

ISBN: (纸本)9798891761681

Previous research on radiology report generation has made significant progress in terms of increasing the clinical accuracy of generated *** this paper, we emphasize another crucial quality that it should possess, i.e., inter-report consistency, which refers to the capability of generating consistent reports for semantically equivalent *** quality is even of greater significance than the overall report accuracy in terms of ensuring the system's credibility, as a system prone to providing conflicting results would severely erode users' ***, existing approaches struggle to maintain inter-report consistency, exhibiting biases towards common patterns and susceptibility to lesion *** address this issue, we propose ICON, which Improves the inter-report CONsistency of radiology report *** to enhance the system's ability to capture similarities in semantically equivalent lesions, our approach first involves extracting lesions from input images and examining their ***, we introduce a lesion-aware mixup technique to ensure that the representations of the semantically equivalent lesions align with the same attributes, achieved through a linear combination during the training *** experiments on three publicly available chest X-ray datasets verify the effectiveness of our approach, both in terms of improving the consistency and accuracy of the generated reports. © 2024 Association for Computational Linguistics.

关键词： Radiology

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A Hybrid CNN-Brown-Bear Optimization Framework for Enhanced Detection of URL Phishing Attacks

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computers, Materials & Continua 2024年第12期81卷 4853-4874页

作者： Brij B.Gupta Akshat Gaurav Razaz Waheeb Attar Varsha Arya Shavi Bansal Ahmed Alhomoud Kwok Tai Chui Department of Computer Science and Information Engineering Asia UniversityTaichung413TaiwanChina Computer Engineering Ronin InstituteMontclairNJ 07043USA Management Department College of Business AdministrationPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Business Administration Asia UniversityTaichung413TaiwanChina Department of Research and Innovation Insights2TechinfoJaipur302001India Department of Computer Science College of ScienceNorthern Border UniversityArar91431Saudi Arabia Department of Electronic Engineering and Computer Science Hong Kong Metropolitan University(HKMU)Hong KongChina

Phishing attacks are more than two-decade-old attacks that attackers use to steal passwords related to financial *** the first reported incident in 1995,its impact keeps on ***,during COVID-19,due to the increase in digitization,there is an exponential increase in the number of victims of phishing *** deep learning and machine learning techniques are available to detect phishing ***,most of the techniques did not use efficient optimization *** this context,our proposed model used random forest-based techniques to select the best features,and then the Brown-Bear optimization algorithm(BBOA)was used to fine-tune the hyper-parameters of the convolutional neural network(CNN)*** test our model,we used a dataset from Kaggle comprising 11,000+*** addition to that,the dataset also consists of the 30 features that are extracted from the website uniform resource locator(URL).The target variable has two classes:“Safe”and“Phishing.”Due to the use of BBOA,our proposed model detects malicious URLs with an accuracy of 93%and a precision of 92%.In addition,comparing our model with standard techniques,such as GRU(Gated Recurrent Unit),LSTM(Long Short-Term Memory),RNN(Recurrent Neural Network),ANN(Artificial Neural Network),SVM(Support Vector Machine),and LR(Logistic Regression),presents the effectiveness of our proposed ***,the comparison with past literature showcases the contribution and novelty of our proposed model.

关键词： Phishing attack CNN brown-bear optimization

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Med-DDoS-SDN: A Kafka Streams-Based Distributed Approach for Protecting Healthcare SDN Environments Against DDoS Attacks

Med-DDoS-SDN: A Kafka Streams-Based Distributed Approach for...

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Congress on Smart computing Technologies, CSCT 2023

作者： Kaur, Amandeep Krishna, C. Rama Patil, Nilesh Vishwasrao Department of Computer Science and Engineering National Institute of Technical Teachers Training and Research Chandigarh India Department of Computer Engineering Government Polytechnic Chhatrapati Sambhaji Nagar Aurangabad India

ISBN: (纸本)9789819780952

The healthcare sector, dealing with vast and sensitive data, faces an ever-expanding need for efficient data management and security. Automated healthcare devices generate crucial information, and Software-Defined Networking (SDN) has become integral to healthcare systems due to resource efficiency, robust monitoring, and centralized control. However, in this data-centric environment, SDNs are vulnerable to various cyber threats, including Distributed Denial of Service (DDoS) attacks and probing attempts. These attacks can harm network performance, potentially endangering lives, especially with the rise of portable healthcare integrating mobile services and programmable devices. This article proposes a Kafka Streams-based distributed approach to protect SDN-based healthcare systems from DDoS attacks, named Med-DDoS-SDN. The Med-DDoS-SDN comprises two modules: (i) Real-time Attack Traffic Classification Module (RACM) and (ii) Preprocessed Data Storage Module (PDSM). The RACM categorizes incoming network traffic into four real-time classes. PDSM collects 21 essential features with outcomes into the Hadoop Distributed File System (HDFS) to maintain the classification model with the range samples. Med-DDoS-SDN is formulated and verified using the CICDDoS2019 dataset. The average classification accuracy of the distributed Med-DDoS-SDN solution stands at 92.75%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Information management

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Blockchain-based Patient Recommendation System for Smart Healthcare

Blockchain-based Patient Recommendation System for Smart Hea...

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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Mehta, Raj Mehta, Mahek Kakkar, Riya Oza, Parita Agrawal, Smita Tanwar, Sudeep Dutta, Ashit Kumar Rodrigues, Joel J.P.C. Department of Computer Science and Engineering Institute of Technology Nirma University Gujarat 382481 India Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Saudi Arabia

ISBN: (纸本)9798350350548

In recent times data breaches in various sectors of industry have become a common threat. It has become very crucial to secure patient data in the health industry. The upcoming Healthcare 4.0 techniques can play an important role in this. We have implemented these techniques in our proposed model to protect personalised health information of the individual health profiles of the patient using blockchain. The proposed model is also a recommending system with the aim to offer relevant advice to the patients to keep a check on the various health parameters like blood pressure, body temperature, blood sugar etc. © 2024 IEEE.

关键词： Electronic health record

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High Resolution Solar Image Generation Using Generative Adversarial Networks

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Annals of Data science 2024年第5期11卷 1545-1561页

作者： Dash, Ankan Ye, Junyi Wang, Guiling Jin, Huiran Department of Computer Science New Jersey Institute of Technology NewarkNJ07102 United States School of Applied Engineering and Technology New Jersey Institute of Technology NewarkNJ07102 United States

We applied Deep Learning algorithm known as Generative Adversarial Networks (GANs) to perform solar image-to-image translation. That is, from Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI) line of sight magnetogram images to SDO/Atmospheric Imaging Assembly (AIA) 0304-Å images. The Ultraviolet (UV)/Extreme Ultraviolet observations like the SDO/AIA 0304-Å images were only made available to scientists in the late 1990s even though the magnetic field observations like the SDO/HMI have been available since the 1970s. Therefore, by leveraging Deep Learning algorithms like GANs we can give scientists access to complete datasets for analysis. For generating high resolution solar images, we use the Pix2PixHD and Pix2Pix algorithms. The Pix2PixHD algorithm was specifically designed for high resolution image generation tasks, and the Pix2Pix algorithm is by far the most widely used image to image translation algorithm. For training and testing we used the data for the year 2012, 2013 and 2014. After model training, we evaluated the model on the test data. The results show that our deep learning models are capable of generating high resolution (1024 × 1024 pixels) SDO/AIA0304 images from SDO/HMI line of sight magnetograms. Specifically, the pixel-to-pixel Pearson Correlation Coefficient of the images generated by Pix2PixHD and original images is as high as 0.99. The number is 0.962 if Pix2Pix is used to generate images. The results we get for our Pix2PixHD model is better than the results obtained by previous works done by others to generate SDO/AIA 0304 images. Thus, we can use these models to generate AIA0304 images when the AIA0304 data is not available which can be used for understanding space weather and giving researchers the capability to predict solar events such as Solar Flares and Coronal Mass Ejections. As far as we know, our work is the first attempt to leverage Pix2PixHD algorithm for SDO/HMI to SDO/AIA0304 image-to-image translation. © Th

关键词： Generative adversarial networks

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Enhanced Reliability in Network Function Virtualization by Hybrid Hexagon-Cost Efficient Algorithm

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computer Systems science & engineering 2022年第12期43卷 1131-1144页

作者： D.Jeyakumar C.Rajabhushanam Department of Computer Science and Engineering Bharath Institute of Higher Education and ResearchChennai600126India

In this,communication world, the Network Function Virtualization concept is utilized for many businesses, small services to virtualize the network nodefunction and to build a block that may connect the chain, communication ***, Virtualized Network Function Forwarding Graph (VNF-FG) has beenused to define the connection between the VNF and to give the best end-to-endservices. In the existing method, VNF mapping and backup VNF were proposedbut there was no profit and reliability improvement of the backup and mapping ofthe primary VNF. As a consequence, this paper offers a Hybrid Hexagon-CostEfficient algorithm for determining the best VNF among multiple VNF and backing up the best VNF, lowering backup costs while increasing dependability. TheVNF is chosen based on the highest cost-aware important measure (CIM) rate,which is used to assess the relevance of the VNF forwarding *** achieveoptimal cost-efficiency, VNF with the maximum CIM is selected. After the selection process, updating is processed by three steps which include one backup VNFfrom one SFC, two backup VNF from one Service Function Chain (SFC),and twobackup VNF from different SFC. Finally, this proposed method is compared withCERA, MinCost, MaxRbyInr based on backup cost, number of used PN nodes,SFC request utility, and latency. The simulation result shows that the proposedmethod cuts down the backup cost and computation time by 57% and 45% compared with the CER scheme and improves the cost-efficiency. As a result, this proposed system achieves less backup cost, high reliability, and low timeconsumption which can improve the Virtualized Network Function operation.

关键词： Network function virtualized(NFV) hybrid hexagon-cost efficient algorithm(HH-CE) cost-aware important measure(CIM) reliability network services

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Advanced transformer model with fine-grained correlation fusion for Multimodal Emotion analysis 2

Advanced transformer model with fine-grained correlation fus...

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2nd IEEE International Conference on Emerging research in Computational science, ICERCS 2024

作者： Krishnan, Sundara Rajulu Navaneetha Sangeetha, M. Srinivasulu, Senduru Surendran, R. Dhanalakshmi Srinivasan University Trichy Department of Computer Science and Engineering Samayapuram Campus Tiruchirappalli India Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Computer Science and Engineering Avadi Chennai India Sathyabama Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai India Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Saveetha School of Engineering Chennai India

ISBN: (纸本)9798331534950

In recent days, Emotion recognition has garnered a lot of attention because of its important applications in human-computer interaction. Human emotion is expressed through a variety of verbal and nonverbal languages, including text, audio, and visual. Current multimodal emotion analysis techniques typically perform poorly because they don't fully utilize the link between various modalities. An advanced transformer model with fine-grained correlation fusion (AT-FGCF) is presented in this research to identify emotion in text, audio, and visual modalities. To enhance the representation of the input audio and video, dense hierarchical feature maps are captured using the Dense Swin Transformer (DSwinT) model. Also, an Emotion- Aware Cognitive BERT (EAC-BERT) is introduced to enhance text representation. Lastly, a fine-grained correlation fusion is carried out to take advantage of correlations between fine-grained features from multimodal data. Our experimental findings show that the suggested method is successful in reaching radical outcomes across two benchmark datasets. That is the datasets IEMOCAP, and MELD all have average accuracy improvements of about 88.35%, and 84.80% respectively. © 2024 IEEE.

关键词： Emotion Recognition

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Early-Stage Disease Prediction in Chilli Plant Using YOLO Models 2

Early-Stage Disease Prediction in Chilli Plant Using YOLO Mo...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Manoj, R. Girish, Sandra Pushpa, B.R. Rani, N. Shobha Sangamesha, D. Department of Computer Science School of Computing Amrita Vishwa Vidyapeetham Mysuru India Department of Computer Science and Engineering Gitam School of Technology Gitam University Bengaluru India Department of Chemistry The National Institute of Engineering Mysore India

ISBN: (纸本)9798350383867

Agriculture is an important component of every country's economy, supplying the necessary resources to farmers and their families. The livelihoods of farmers are greatly threatened by crop diseases, which highlights the importance of early identification of diseases in chilli plants at the right stage of growth is crucial for timely fertilizer recommendation. This paper presents an intelligent transfer learning technique to detect thrips and viruses in chilli plants in the early stages of development. Stage-by-stage datasets are collected from chilli plant orchards around Mysore district, Karnataka, to carry out the training using the YOLO model. The dataset consists of three growth stages of chilli plants (15, 25, and 35 days) that are captured in diverse environmental settings with multiple-resolution smart phones. The annotations are created for the diseases healthy, viruses, and thrips at leaf level from whole plants for all three stages. Hyperparameters such as learning rate, the learning optimization algorithm, batch size, epochs, and loss functions are employed to fine-tune the YOLO v5, v6, and v7 models. To achieve high generalisation and reduce overfitting, a weight decay (weight_decay) of 0.0005 is employed. From the evaluation, it was noticed that YOLOv7 outperformed other YOLO models. A mAP score of 0.80 is achieved in stage 1, 0.82 in stage 2, and 0.73 in stage 3 with minimal validation loss and remarkable inference speed. © 2024 IEEE.

关键词： Plant diseases

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Predicting the Deaths rate of COVID-19 Using Big Data Analytics and Machine Learning 14

Predicting the Deaths rate of COVID-19 Using Big Data Analyt...

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14th International Conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Laxmikant, Kumar Anitha, G. Usman, Sadam Mohamed Amrita School of Computing Department of Computer Science and Engineering Chennai India Stevens Institute of Technology Department of Computer Science HobokenNJ United States

ISBN: (纸本)9798350335095

The global repercussions brought about by the COVID-19 outbreak has been felt by countless individuals worldwide, leading to the execution of an extensive community health strategy aimed at curtailing the transmission of the virus. Large-scale datasets from numerous nations underwent thorough statistical analysis and modeling procedures using advanced techniques such as big data analytics and machine learning. This research paper delves into the utilization of extensive data analysis and harnessing the power of machine learning techniques for predicting the fatality rate linked to COVID-19. After a comparative analysis, the Multiple Linear Regression The algorithm has demonstrated its superiority by delivering outstanding performance. With its strong ability to adapt to different scenarios, the suggested approach achieves an impressive correctness rate of 98.6% and a high R-Squared score of 0.987. © 2023 IEEE.

关键词： COVID-19

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