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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Samuel Rufus, J. Sai Kishore, S. Lakshmi Priya, S. Sathyabama Institute of Science And Technology Computer Science And Engineering Chennai India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359299

This project, "Enhancing Trust in Currency Transactions: Currency Note Authentication with Transfer Learning,"employs transfer learning in machine learning to improve the accuracy and efficiency of authenticating physical currency notes. By leveraging pre-trained deep learning models, the system distinguishes genuine from counterfeit notes, bolstering trust in currency transactions. The research includes developing a transfer learning framework, creating datasets, and evaluating the system's effectiveness against counterfeit scenarios. This work contributes to enhancing the security of currency transactions in our digital financial landscape. © 2024 IEEE.

关键词： Deep learning

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Emotion Detection by Employing Deep Learning CNN Model 2

Emotion Detection by Employing Deep Learning CNN Model

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Asha, P. Vipulendiran, Aararan Kumaravelu, Abishek Refonaa, J. Jany Shabu, S.L. Joshila Grace, L.K. Sathyabama Institute Of Science And Technology Computer Science And Engineering Chennai India

ISBN: (纸本)9798350381665

Facial expression recognition through computer algorithms presents many challenges, despite its innate human capability. This study compares some of the most renowned methods used in Emotion detection and proposes VGG-16 as the most tailored method for emotional recognition, emphasizing the importance of facial expressions as emotional indicators. Leveraging recent advancements, including Robert Horling's cognitive processes, this integrated system adeptly discerns the subjects' moods. While electroencephalography remains a formidable tool, the method proposed by this research paper offers a cost-effective alternative for emotion recognition. Through comprehensive comparative studies and empirical evaluations, this research demonstrates VGG-16's superior performance in terms of both training speed and accuracy, surpassing alternative models such as ResNet-50 and AlexNet. VGG-16 achieved an accuracy rate exceeding 71%, outperforming ResNet- 50 with 68% accuracy and AlexNet with approximately 64% accuracy. The results show the efficacy of VGG-16 in the context of training efficiency and accuracy, substantiating its merit as a robust model for emotion detection in comparison to alternative architectures. This research opens avenues for real-world applications in diversified sectors such as education, healthcare, and HCI (Human-computer interaction). © 2024 IEEE.

关键词： Emotion Recognition

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Property Price Prediction 3

Property Price Prediction

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Robin, Aleena Anna Prasad, Gouri Jiji, Jyothsna Raajesh, Sanjay Sheena, K.V. Muthoot Institute of Technology and Science Department of Computer Science and Engineering India

ISBN: (纸本)9798350354171

This data analytics project is designed for the real estate industry, with the primary aim of predicting property prices, including lands, buildings, and houses linear regression model and also by carefully analyzing and interpreting current market trends. Although numerous existing systems share the common objective of property price prediction, our proposed system brings distinct advantages. One of them is its ability to explore rental options for rooms and buildings. What truly sets our system apart is its comprehensive approach to property valuation. It achieves this by incorporating a user-specific data analysis component, which considers critical factors like user loan availability. Essentially, it bridges the gap between the realm of property investment and the realm of financial planning, equipping users with essential insights to make sound financial decisions in the context of real estate. This project has been meticulously designed with the unique needs of the real estate industry in mind, offering a transformative tool that assists individuals in making smarter choices concerning property investments, rentals, and loans. By providing a comprehensive, user-focused approach, it paves the way for more informed decision-making in the ever-evolving landscape of the real estate market, ultimately promoting financial prudence and responsible financial planning. © 2024 IEEE.

关键词： Data analytics project Linear regression Market trends analysis Predicting property prices Real estate industry Rental options exploration

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Blockchain Integration in Emergency Medical Transportation: Advancing Security and Coordination for Critical Care Scenarios 5th

Blockchain Integration in Emergency Medical Transportation: ...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Rawat, Abhishek Suryavanshi, Raghuraj Singh Nagar, Vishal Yadav, Divakar Department of Computer Science and Engineering Pranveer Singh Institute of Technology Uttar Pradesh Kanpur India Department of Computer Science and Engineering Institute of Engineering and Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819771776

Blockchains improve safety and faster teamwork for emergency medical transport in critical care. Emergency medical transport is not the only healthcare sector that has become better and different because of current blockchains. This report also illustrates how blockchains can alter the safety, integrity, and effectiveness of emergency medical transport operations, including a wide-ranging examination of these technologies’ consequences on the healthcare sector. Traditional Emergency Medical Transportation Systems face different challenges, and this paper provides a framework for a complete understanding of the possible benefits and creative solutions that Blockchain offers to the healthcare industry. This paper has given a compelling business example for applying Blockchain to enhance Emergency Medical Transportation. It gives valuable perspectives on smart steps that can be used to improve care and well-being and streamline pool offerings and a feasibility rate that could help to create an effective outcome for Emergency Medical Transport Services with the usage of Blockchain. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Blockchain Healthcare industry Medical Data History Transportation

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Task scheduling and data replication in cloud with improved correlation strategy

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International Journal of computers and Applications 2023年第11期45卷 697-708页

作者： Rambabu, D. Govardhan, A. Department of Computer Science and Engineering Sreenidhi Institute of Science and Technology Hyderabad India Department of Computer Science and Engineering Jawaharlal Nehru Technological University Hyderabad Hyderabad India

Cloud providers frequently utilize two tightly coupled resource management strategies like task scheduling & data replication to boost the performance of the system generally, guaranteeing service level agreement (SLA) compliance, as well as protecting their own financial gain. An Improved Correlation strategy-based Task Scheduling and Data Replication in Cloud (ICTSDC) is what this work aims to give. The suggested system's primary phases are as follows: Management of replication and task scheduling. Initial job scheduling will be optimization-based and take into account goals such bottleneck value, migration cost, VM load, enhanced correlation, and replication, respectively. For this, a brand-new extended DMO algorithm called Self-adaptive Dwarf Mongoose Optimization (SADMO) is presented. In the replication management stage, the potential copies must first be identified based on the prior objective. The suggested SADMO model implements the optimization technique for replica placement throughout the replication management process. The outcomes of the ICTSDC technique are evaluated to other methods using a variety of metrics, like bottleneck value, migration cost, Virtual Machine (VM) load, improved correlation, as well as replication efficiency. A lower mean value of 0.324 is gained with the ICTSDC scheme for fitness. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Virtual machine

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Optimizing Waste Management: Integrated Pollution Detection and Systematic Reporting for Sustainable Disposal 2

Optimizing Waste Management: Integrated Pollution Detection ...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Samuel Prabakaran, M. Sophia, S. Sophas Samuel, S. Karunya Institute of Technology and Sciences Department of Computer Engineering Coimbatore India Karunya Institute of Technology and Science Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365269

Effective waste management and pollution control are paramount for sustainable environmental stewardship. This study presents a comprehensive approach leveraging cutting-edge technologies such as YOLO object recognition, Faster R-CNN, and SVM-based AQI computation. The system integrates YOLOv7 for real-time rubbish classification, facilitating swift and precise trash sorting and disposal decisions. Additionally, it employs Faster R-CNN for fog density analysis, enabling thorough estimations of air pollution levels. To calculate the Air Quality Index (AQI), support vector machine (SVM) algorithms combine various air quality indicators, ensuring thorough and accurate assessments. Enhanced trash classification capabilities enable more accurate evaluation of the environmental impacts of waste management systems. By integrating fog density monitoring with AQI calculation, the approach offers comprehensive trend analysis of pollution, aiding in the formulation of data-driven environmental policies and effective pollution management measures. Furthermore, the system evaluates soil quality, complementing its waste management and pollution control functions. Despite its advancements, the system faces challenges in real-world implementation, including data acquisition, model training, and system integration. Addressing these challenges is critical for realizing the full potential of integrated waste management and pollution control systems. © 2024 IEEE.

关键词： Data acquisition

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An approach to on-stream DDoS blitz detection using machine learning algorithms

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Materials Today: Proceedings 2023年 80卷 3492-3499页

作者： Manjula, H.T. Neha Mangla Atria Institute Of Technology Bengaluru India Department Of Computer Science & Engineering CMRIT Bengaluru India Department of Information Science & Engineering Atria Institute of Technology Bengaluru India

Distributed Denial of service (DDoS) attacks is an enormous threat to today's cyber world, cyber networks are compromised by the attackers to distribute attacks in a large volume by denying the service to legitimate users. The toughest and challenging task in today's network and network security engineers is to identify compromised traffic (attacked) and legitimate (normal) traffic. The main goal of the paper is to detect DDos attacks using classification algorithms. To achieve the goal the proposed system uses attacking tool to initiate attacks using Loic attacking tool with the data set extracted from open source tool Wireshark and transferring the dataset to apache Spark for detection analysis. The system also uses Apache spark machine learning algorithms (MLib), classification algorithms to classify the dataset. We use Naive Bayes, KNN and Random forest classification algorithms to classify normal traffic and attacked traffic. Our system is capable of detecting attacks with respect to any traffic protocols ICMP, TCP, or UDP. The accuracy of detection is compared on three classification algorithms and noted that random forest gives the accuracy of 96.75%. © 2021

关键词： Denial-of-service attack

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Credit card fraud detection with formula-based authentication 5th

Credit card fraud detection with formula-based authenticatio...

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5th International Conference on Advances in AI for Biomedical Instrumentation, Electronics and Computing, ICABEC 2023

作者： Asha, P. Babu, Bhavya Mary, S. Prince Ankayarkanni, B. Praveena, M.D. Anto Christy, A. Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9781032642987

Nowadays, digital withdrawals mostly rely on cards. Days have come where people don’t have to carry cash in their pockets and just a small card is enough to make all the withdrawals. Problems with cards will lead to passwords being hacked and easily lead to fraud. Credit card fraud can be identified using Hidden Markov Model (HMM) and Formula Based Authentication. In the Existing system, card withdrawals are very routine among the people and the frauds corresponding to the improvement of security are increasing. In the proposed system, ML algorithms have been applied to detect MasterCard deception in a disproportionate dataset. In the modification process, an application is developed for a banking sector particularly for a credit or ATM card. Users can create an account and get the ATM card along with a unique formula which should be used during suspicious transactions. The user behavior of every transaction is tracked by Hidden Markov Model and if there are any occurrences of suspicious transactions, then a message is sent to the user with the keys that are required to complete the formula. After the user applies the keys to the formula the solution must be entered as the password in order to complete the transaction. © 2024 The Author(s).

关键词： Hidden Markov models

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Cyberbullying Predictive Analysis on X Data with Multi Model Supervised Technique 3

Cyberbullying Predictive Analysis on X Data with Multi Model...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Samsun, S. Basil Xavier, S. Karunya Institute of Technology and Science Coimbatore Computer Science and Engineering India

ISBN: (纸本)9798350375190

This research introduces a multi-model supervised predictive analytic method for X cyberbullying detection. The overarching goal of this study is to find a way to effectively detect and categorize cyberbullying incidents on social media sites before they get out of hand. This study has gathered, cleaned, and turned a dataset of cyberbully-ingrelated tweets in X platform into numerical attributes. In order to forecast if tweets included cyberbullying, three ML models - Support Vector Machines (SVM), Random Forest (RF), and Deep Neural Networks (DNN) - were used. Accuracy, precision, recall, and F1-score were all highly achieved, proving that the suggested method was beneficial. To further explore how various feature engineering strategies affected model results, more tests were carried out. Cyberbullying detection tasks should take into account a variety of language and context-based indicators, according to the research. In conclusion, this research adds to the continuing endeavors to create preventative steps to fight cyberbullying by offering new perspectives and possible ways to keep an eye on and handle this common problem. © 2024 IEEE.

关键词： Support vector machines

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Interactive Learning with AlphAR: A Symbol-Recognition Aid for Dyslexic Children Through Augmented Reality

Interactive Learning with AlphAR: A Symbol-Recognition Aid f...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Shilbi, Susan Chhirolya, Vaidik Basha, Niha Kamal Surendiran, B. School of Computer Science and Engineering Vellore Institute of Technology Vellore India National Institute of Technology Puducherry Department of Computer Science and Engineering Karikal India

ISBN: (纸本)9798331540685

Dyslexia is a neurological condition that affects a person's ability to read, write, and spell. It is characterized by difficulties in processing phonological information, leading to challenges in acquiring fluent and accurate reading skills. Early assistance fosters a positive learning trajectory, enhancing the overall well-being and confidence of dyslexic individuals. AlphAR is an innovative augmented reality (AR) web system specifically tailored to address the challenges of symbol recognition in dyslexic children, developed using cutting-edge A-Frame and *** technologies. It harnesses marker-based AR to create an immersive learning environment. The novelty lies in its seamless integration of marker patterns with corresponding 3D models, facilitating interactive learning experiences for children aged 5 to 10. Through intuitive user interface elements and audio cues, AlphAR enhances engagement and promotes auditory learning. Ethical design principles ensure inclusivity and user-friendliness, prioritizing participant comfort and engagement. Users interact with the system through intuitive gestures, such as flipping down and left, further enhancing skill development. The iterative nature of AlphAR allows for adaptability to individual learning paces, fostering a positive and inclusive experimental environment. To validate AlphAR's effectiveness, we will benchmark it against established AR interventions like LitAR, AHA System and many others. © 2024 IEEE.

关键词： Contrastive Learning

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