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4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Saini, Renu Paras Tayal, Parag Thakur, Pallavi Computer Science and Engineering Department Chandigarh Engineering College Mohali Punjab Landran India

ISBN: (纸本)9781032911571

Cloud computing, which moves assets from desktop setting to cloud infra-structure—where apps are kept in a "cloud" with massive processing power and nearly infinite database storage—has fundamentally altered traditional computing over the past few years. Modern healthcare can benefit greatly from telemedicine when it is combined with cloud computing. Strategic thinking is necessary before moving healthcare assets and services to the cloud in order to weigh the advantages and disadvantages of the fresh approach, determine whether it may be implemented, and analyze its capabilities. The study illustrated how various technologies could be applied to big data analysis in healthcare using a cloud computing. In this research, we investigate the way cloud computing has changed the way in which healthcare organizations keep their data. © 2025 the Author(s).

关键词： Electronic health record

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Signbridge-Audio to Sign Language Translator

Signbridge-Audio to Sign Language Translator

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Shirisha, Kammadanam Deeksith, E. Mani Madhava, M. S. S Sri Surendhar, S. Karthikeyan, R. Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Hyderabad India

ISBN: (纸本)9798331529833

The lack of communication options for Deaf and hearing people, some may say creates a significant social disadvantage in accessing the often-bare essential services. In contrast to acoustically communicated sound patterns, sign language communicates ideas freely through manual and body language. It can be used by those who have trouble speaking, by those who can hear but cannot talk, and by regular people to interact with those with hearing impairments. By automatically converting spoken words into hand gestures, this project creates a web-based interface that allows hearing-impaired individuals to communicate with normal people in real time through sign language interpretation. Two major phases of the system Speech-to-text technology interprets oral input into textual output. Next, the text is run through Natural Language Processing (NLP) algorithms leveraging the Natural language toolkit (NLTK) to be syntactically parsed in the context of sign natural rules. The last phase translates the parsed text into sign language gestures which involve hand shapes, orientation, and body movements to convey the visual meaning of a message. This system could be used to greatly reduce the communication barriers experienced by people with hearing loss and deafness using Machine Learning (ML) to continuously improve accuracy, enhance quality of life, and provide a more inclusive society between the deaf community in our daily interactions within an ever-growing world. © 2025 IEEE.

关键词： Natural language processing systems

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Blockchain and Data Integrity Authentication Technique for Secure Cloud Environment

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Intelligent Automation & Soft Computing 2023年第5期36卷 2055-2070页

作者： A.Ramachandran P.Ramadevi Ahmed Alkhayyat Yousif Kerrar Yousif Department of Computer Science and Engineering University College of EngineeringPanruti607106India Department of Electronics and Communication Engineering University College of EngineeringBIT CampusAnna UniversityTiruchirapalli620025India College of Technical Engineering The Islamic UniversityNajafIraq Department of Computer Technical Engineering Al-Hadba University CollegeMosulIraq

Nowadays,numerous applications are associated with cloud and user data gets collected globally and stored in cloud *** addition to shared data storage,cloud computing technique offers multiple advantages for the user through different distribution designs like hybrid cloud,public cloud,community cloud and private *** cloud-based computing solutions are highly con-venient to the users,it also brings a challenge i.e.,security of the data ***,in current research paper,blockchain with data integrity authentication technique is developed for an efﬁcient and secure operation with user authentica-tion *** technology is utilized in this study to enable efﬁcient and secure operation which not only empowers cloud security but also avoids threats and ***,the data integrity authentication technique is also uti-lized to limit the unwanted access of data in cloud storage *** major objec-tive of the projected technique is to empower data security and user authentication in cloud computing *** improve the proposed authentication pro-cess,cuckooﬁlter and Merkle Hash Tree(MHT)are *** proposed meth-odology was validated using few performance metrics such as processing time,uploading time,downloading time,authentication time,consensus time,waiting time,initialization time,in addition to storage *** proposed method was compared with conventional cloud security techniques and the outcomes establish the supremacy of the proposed method.

关键词： Blockchain security data integrity authentication cloud computing signature hash tree

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Optimized Hybrid Deep Learning Approach for Detecting DoS Attacks Using Feature Selection in Wireless Sensor Networks

Optimized Hybrid Deep Learning Approach for Detecting DoS At...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Sasson Taffwin Moses, S. Abraham David, L. Emil Selvan, G.S.R. Ramkumar, M.P. Thiagarajar College of Engineering Department of Computer Science and Engineering Tamil Nadu Madurai India

ISBN: (纸本)9798331512965

The rapid growth of smart cities, healthcare monitoring, and environmental sensing relies heavily on the real-time data processing capabilities of Wireless Sensor Networks (WSNs). However, these networks face significant security challenges, particularly from malicious activities like Denial of Service(DoS) attacks, which can disrupt critical operations. A hybrid intrusion detection model that combines Convolutional Neural Networks (CNN) and Deep Neural Networks (DNN) to improve the detection of such threats has been proposed. The preprocessing phase includes the Synthetic Minority Over-sampling Technique (SMOTE) which mitigates class imbalances ensuring robust performance across diverse attack scenarios. The model incorporates advanced feature selection techniques including SelectKBest and Grey Wolf Optimization (GWO) to identify the most critical features for enhanced detection accuracy. The CNN component effectively captures local traffic patterns, which are then refined by the DNN for accurate classification. This hybrid model's effectiveness in detecting diverse DoS attack scenarios is evaluated using WSN-DS dataset, ensuring robust security for WSNs. By achieving an impressive 98% detection accuracy, this hybrid model proves to be effective in intrusion detection. Its applications can significantly strengthen the security and reliability of WSNs, ensuring uninterrupted operations in key sectors such as smart cities, healthcare, and environmental monitoring. © 2025 IEEE.

关键词： Network intrusion

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Experimental investigation on machining of titanium composite using artificial neural network

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Interactions 2025年第1期246卷 1-10页

作者： Perumal, B. Kannan, R. Arunkumar, R. Kumaresan, T. Subbiah, Ram Department of Mechanical Engineering Sri Venkateshwara College of Engineering Andhra Pradesh Tirupati India Department of Mechanical Engineering PSNA College of Engineering and Technology Dindigul India Department of Mechanical Engineering MRK Institute of Technology Tamilnadu Kattumannarkoil India Department of Computer Science and Engineering Bannari Amman Institute of Technology Sathyamangalam Erode India Department of Mechanical Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India

Manufacturers must be able to figure out the most suitable technique capable of generating rapid and accurate performance when developing a precise modelling approach for the development of an efficient machining process. This paper assesses the predictive capabilities of the Artificial Neural Network (ANN) and the response surface methodology in the titanium machining process with respect to tool tip temperature and tool wear. The adequacy of the ANN in modeling and forecasting reactions were rigorously examined and contrasted using data collected from properly prepared machining experiments. Both approaches performed admirably in terms of forecasting machining process responses. The coefficient of correlation (R2) obtained from the analysis verifies the ANN’s choice, with a maximum value of 99.9% and with the response surface methodology with a maximum value of 99.8% was found. The experiment also demonstrates that when the proper parameters are used, the ANN approach can produce the best results. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Machining centers

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Employing Vision Transformer for Monocular Depth Estimation 2

Employing Vision Transformer for Monocular Depth Estimation

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Chaudhary, Anubhav Tripathi, Dev Brat Mishra, Harsh Pathak, Prasoon Dixit, Ashish Department of Computer Science and Engineering Ajay Kumar Garg Engineering College Ghaziabad India

ISBN: (纸本)9798331530389

Monocular depth estimation (MDE) is an important task in computer vision, it enables a range of applications like robotic navigation, augmented reality and also used in surgical guidance. This paper shows the use of ViTs for MDE and focuses on the Depth Anything V2(DA-V2) model. DAV2 model leverages a teacher-student training approach and uses the synthetic and unlabeled real images which are designed to enhance the accuracy of depth estimation. DA-V2 is fine-tuned on EndoSLAM dataset and contains challenging visual scenes from endoscopic images of the stomach, colon and small intestine to check the adaptability of the model to medical depth estimation. Our experiment shows that the fine-tuned DA-V2 outperforms all the conventional models like Endo-SfMLearner and achieves reductions in root mean squared error(RMSE) and absolute relative error, across all anatomical datasets. DA-V2 achieves the RMSE values 1.245, 1.028, and 2.021 for the stomach, colon, and intestine datasets, respectively as compared to the SfMLearner's 6.387, 6.657, and 14.787. The findings show that ViTs are capable of achieving higher accuracy and generalization in monocular depth estimation tasks. This study highlights the effectiveness of DA-V2 and Vision Transformers in Monocular Depth Estimation and opens up possibilities for further model optimization. © 2025 IEEE.

关键词： Convolutional neural networks

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High Degree Dependency Study of Ensemble Models in Credit Card Fraud Analysis 2

High Degree Dependency Study of Ensemble Models in Credit Ca...

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Rani, Shalini Tiwari, Mayank Tyagi, Shiva Department of Computer Science and Engineering Ajay Kumar Garg Engineering College Ghaziabad India

ISBN: (纸本)9798331530389

Credit card fraud detection is an increasingly critical issue due to the growth of digital transactions and the sophistication of fraudulent activities. This study proposes a hybrid framework combining Graph Neural Networks (GNNs), ensemble methods (e.g., XGBoost, Random Forest), and polynomial feature engineering to address key challenges, including class imbalance, non-linear data relationships, and real-time applicability. The system achieved a detection accuracy of 96.7%, precision of 96.9%, recall of 96.4%, and a false-negative rate of 3.6% on benchmark datasets. These improvements are attributed to the integration of advanced sampling techniques, polynomial feature transformations, and the hybrid architecture. The findings demonstrate the effectiveness of the proposed approach in enhancing fraud detection systems and provide a scalable solution for real-world applications. © 2025 IEEE.

关键词： Graph neural networks

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A Secure and Efficient Cloud Storage System Using Advanced Encryption Standard Algorithm for Data Protection 5th

A Secure and Efficient Cloud Storage System Using Advanced E...

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5th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Babu, Gurujukota Ramesh Chintalapati, Phaneendra Varma Sree, Pokkuluri Kiran Satish Kumar, Kode Asha, A.V.S. Maneesha, Boddu Department of Computer Science and Engineering Shri Vishnu Engineering College for Women Bhimavaram India

ISBN: (纸本)9783031770777

The Cloud Computing (CC) is a model which treats the resources as an integrated entity on the internet, cloud. Cloud computing is an unique environment or network in which process, access and maintenance are done by anywhere from the world. The CC is a customized internet-based web server. Data security is receiving more and more attention with the expanding usage of cloud storage systems and due to its challenging circumstances. Any cloud data storage model’s fundamental objective is to provide simple data access without compromising the security of the data. The main concerns of any cloud storage system are ensuring safety and security. Big data security and privacy have emerged as a problem that prevents the usage of cloud services by the organization. Earlier privacy-preserving methods had a number of drawbacks includes completely dependence on third parties, a lack of performance efficiency, accurate data analysis, and data privacy. This analysis presents a secure and efficient cloud storage system that uses the advanced encryption standard algorithm for data protection. The user files are encrypted with the AES (Advanced Encryption Standard) standard. The efficiency of the method that is being provided is evaluated in terms of execution, decryption, and encryption times. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Cloud security

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EDUVERSE: Inclusive and Immersive Metaverse Learning Environment for Education

EDUVERSE: Inclusive and Immersive Metaverse Learning Environ...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Madhubala, R. Gomathi, V. Jothikumar, Karthikeyan National Engineering College Department of Computer Science and Engineering Tamil Nadu Kovilpatti India

ISBN: (纸本)9798331529833

Eduverse is an advanced Virtual Reality (VR) platform developed to transform traditional education by creating interactive and immersive virtual learning environments. Designed to support both students and teachers, Eduverse simulates real-world classrooms, discussion rooms, and conference halls, enabling real-time communication and collaboration. One of its standout features is the accessibility tools, such as text-to-speech (TTS) and speech-to-text (STT) systems, which make the platform highly beneficial for students with disabilities. The TTS system helps students with speech impairments by converting their typed messages into spoken words, while the STT system transcribes spoken communication into text for students who have difficulty *** addition to accessibility, Eduverse offers customizable avatars, allowing students to represent themselves digitally, which helps reduce social anxiety and encourages participation. The platform also provides teachers with speech management tools, enabling them to moderate discussions, assign speaking privileges, and manage class activities. This ensures that every student has the opportunity to contribute, even in larger classes. Eduverse has been tested within a set of students, showing significant improvements in student engagement, participation, and inclusivity compared to traditional online learning platforms. Students with disabilities reported that the integrated accessibility tools allowed them to engage more fully in class discussions, making Eduverse a valuable solution for inclusive education. By combining a collaborative and supportive learning space, Eduverse demonstrates the potential to improve the way students learn and interact in the virtual world. © 2025 IEEE.

关键词： Students

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A Comprehensive Survey on Traffic Light Detection Sensors for Vehicle Safety and Automatic Emergency Braking Systems Using Deep Learning Techniques 3rd

A Comprehensive Survey on Traffic Light Detection Sensors fo...

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3rd International Conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Gowri, B. Shyamala Priya, G. Vishnu Anjana, S. Arjun, V. Kodal, Devendraa Department of Computer Science and Engineering Easwari Engineering College Tamil Nadu Chennai India

ISBN: (纸本)9789819783540

The integration of Traffic Light Detection (TLD) systems with Advanced Emergency Braking Systems (AEBS) marks a critical milestone in enhancing road safety and paving the way for advanced autonomous driving. This survey paper provides a panoramic and extensive overview of the state-of-the-art TLD solutions leveraging sensors and deep learning techniques. With an increasing emphasis on accident prevention and traffic management, the intersection of TLD and AEBS has become a focal point of research and development. This survey begins by elucidating the fundamental challenges associated with TLD, including varying environmental conditions, occlusions, and complex traffic scenarios. We explore the pivotal role of sensors such as cameras, LiDAR, and radar in providing the requisite data for TLD, and delve into the intricacies of sensor fusion techniques for enhanced perception. Deep Learning has emerged as a cornerstone technology in TLD, enabling robust object detection, classification, and real-time decision-making. We meticulously analyze a spectrum of deep learning architectures including Single-Shot Detectors (SSD), Faster R-CNN, YOLO, and custom-designed networks tailored for TLD applications. Furthermore, the survey examines critical components of the TLD pipeline, encompassing data collection, preprocessing, model training, real-time inference, and integration with AEBS. Emphasis is placed on real-time constraints, multi-modal sensor fusion, and adaptability to diverse traffic light configurations. The paper also delves into the significance of accurate traffic light state prediction, going beyond mere detection to anticipate traffic light changes and optimize vehicle control actions. Human-centric interaction and privacy concerns are addressed, encompassing driver warnings, user interfaces, and data anonymization strategies. Moreover, the survey discusses the importance of safety, validation, and collaboration within the TLD and AEBS ecosystem, emphasizing compl

关键词： Road and street markings

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