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7th International Conference on Digital Medicine and Image Processing, DMIP 2024

作者： Syed Masood, Jafar Ali Ibrahim Vinayagam, Jai Kumar Onteru, Ranjit Kumar Kopparapu, Umamaheswararao Parabathini, Bulah Pushpa Rani Department of IoT School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Centre for Big Data Analytics QIS College of Engineering and Technology Andhra Pradesh Ongole India

ISBN: (纸本)9798400709586

The rapid proliferation of medical big data has opened unprecedented opportunities for enhancing patient outcomes through advanced computational analysis. This paper explores the integration of big data analytics with clinical practices to deliver personalized medicine, optimize treatment protocols, and improve patient care. Our findings demonstrate that a synergistic approach between computational insights and clinical decision-making can significantly advance healthcare outcomes. This study provides a comprehensive framework for harnessing medical big data, addressing key challenges, and proposing solutions to integrate these insights into everyday clinical practice. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Personalized medicine

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Comprehensive Smart Home Automation: Network Setup, Fire Prevention, and Network Security using Cisco Packet Tracer

Comprehensive Smart Home Automation: Network Setup, Fire Pre...

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2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Roy, Bhumika Nivethika, S. Deepa Manju, G. Pandian, M. Senthil School of Computer Science and Engineering Vellore Institute of Technology University Chennai600127 India School of Civil Engineering Vellore Institute of Technology University Chennai600127 India

ISBN: (纸本)9798350394580

Aim: This paper presents a three-phase approach to smart home automation using Cisco Packet Tracer. Phase 1 establishes the foundational network and integrates IoT devices, ensuring successful packet transfer. In Phase 2, the focus shifts to fire detection and prevention, utilizing smoke detectors and automated water sprinklers. Phase 3 enhances network security through firewall implementation, restricting access to specific IP addresses and blocking ICMP and PING messages. These phases collectively create a robust smart home system, combining device integration, safety measures, and network security, offering enhanced convenience and safety for residents. This work aims to use Cisco Packet Tracer and implement it to design a Smart Home for fire detection and prevention, where home appliances are connected to the router, which in turn is connected to the internet and set up a firewall that adds up network security to the system. © 2023 IEEE.

关键词： automation fire alarm home security Smart

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Breast Cancer Diagnosis Using Elephant Herding Optimization and Sparse Autoencoder Through Gene Expression Analysis 15th

Breast Cancer Diagnosis Using Elephant Herding Optimization ...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Bharanidharan, N. Chakravarthy, S. R. Sannasi Kumar, V. Vinoth Kavya, B. Meghana, G. Rajaguru, Harikumar School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Sathyamangalam India Department of Computer Science and Engineering Dayananda Sagar University Bangalore India Department of Computer Science and Engineering PES University RR Campus Bangalore India

ISBN: (纸本)9783031810794

Gene expression analysis is an efficient modality to detect breast cancer. But analysis of the gene expression data is a complicated task due to the involvement of large number of numerical data for each subject. This research work focuses on the development of computerized diagnosis systems for analyzing the gene expression data and classifying the subjects as either normal or breast adenocarcinoma. Sparse autoencoder is used for dimensionality reduction and Elephant Herding Optimization is used for transforming the features. Four different supervised classifiers namely support vector machine, random forest classifier, decision trees, and naive bayes classifier are examined for classification task. Among them, random forest provides the superior balanced accuracy of 86% while it was further enhanced to 92% with the help of sparse autoencoders and elephant herding optimization algorithm. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Transcription

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A Real-Time P2P Bot Host Detection in a Large-Scale Network Using Statistical Network Traffic Features and Apache Spark Streaming Platform 8

A Real-Time P2P Bot Host Detection in a Large-Scale Network ...

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8th IEEE International Conference for Convergence in technology, I2CT 2023

作者： Saravanan, S. Prakash, G. Uma Maheswari, B. Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering India Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350334012

Nowadays, Peer-to-Peer (P2P) bots play a significant role in launching attacks such as phishing, distributed denial-of-service (DDoS), email spam, click fraud, cryptocurrency mining, etc. The analysis of statistical network traffic features of hosts is one of the commonly used methods to detect P2P bots. Modern P2P bot detection systems need to extract features from massive streaming network traffic as the size of the Internet keeps increasing every day. However, traditional detection systems have trouble detecting bots in real-time in large-scale networks as they are not implemented on big data streaming platforms. Hence, this work proposes a network flow-based P2P bot detection system implemented on Apache Spark Structured Streaming Platform to detect P2P bots in real time by analyzing massive streaming network traffic data generated from large-scale networks. Such detection of P2P bots is based on statistical network traffic features: destination diversity ratio, control packets ratio, and total source bytes sent in a flow. There are two components in the proposed system: the first component detects potential P2P hosts using the Destination Diversity Ratio (DDR), and the second component finds out P2P bot hosts from the P2P hosts identified by the first component. Furthermore, the performance of the detection components depends on the time window at which statistical features are extracted. Hence, this work also conducted experiments to study the effect of different time windows on detection components. The proposed system is evaluated using real-world datasets and achieves a True Positive Rate (TPR) of 99.87%. © 2023 IEEE.

关键词： Denial-of-service attack

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Pancreatic Cancer Diagnosis using Swarm Optimization Combined with Machine Learning Techniques 3

Pancreatic Cancer Diagnosis using Swarm Optimization Combine...

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3rd International Conference on Smart Technologies, Communication and Robotics, STCR 2023

作者： Bharanidharan, N. Aggarwal, Pratham Batra, Aryan Shah, Deep Sannasi Chakravarthy, S.R. Rajaguru, Harikumar Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India Bannari Amman Institute of Technology Department of Electronics and Communication Engineering Sathyamangalam India

ISBN: (纸本)9798350370867

When compared to different types of malignant tumors, pancreatic malignancy is the 12th widespread tumor disease among humans globally. Pancreatic cancer can be identified at the earlier stages using microarray gene analysis. The objective of this work is to classify the gene samples as normal and tumoral through the usage of Particle Swarm Optimization technique along with the supervised machine learning classifiers. A variance-based feature selection is employed to select the most appropriate features and the selected features are transformed using particle swarm optimization to improve the classification performance. Four different machine learning supervised algorithms are realized as classification techniques. When these four vanilla classifiers are considered, the random forest algorithm performs relatively well with balanced accuracy score of 87.5%. This score is improved to 94.4% through the usage of the proposed technique. In addition, the proposed technique enhances the prediction of the other two classifiers as well. © 2023 IEEE.

关键词： Gene expression

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Zero-DCE++Inspired Object Detection in Less Illuminated Environment Using Improved YOLOv5

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computers, Materials & Continua 2023年第12期77卷 2751-2769页

作者： Ananthakrishnan Balasundaram Anshuman Mohanty Ayesha Shaik Krishnadoss Pradeep Kedalu Poornachary Vijayakumar Muthu Subash Kavitha Centre for Cyber Physical Systems Vellore Institute of Technology(VIT)ChennaiTamil Nadu600127India School of Computer Science and Engineering Vellore Institute of Technology(VIT)ChennaiTamil Nadu600127India School of Information and Data Sciences Nagasaki UniversityNagasaki8528521Japan

Automated object detection has received the most attention over the *** cases ranging from autonomous driving applications to military surveillance systems,require robust detection of objects in different illumination ***-of-the-art object detectors tend to fare well in object detection during daytime ***,their performance is severely hampered in night light conditions due to poor *** address this challenge,the manuscript proposes an improved YOLOv5-based object detection framework for effective detection in unevenly illuminated nighttime ***,the preprocessing strategies involve using the Zero-DCE++approach to enhance lowlight *** is followed by optimizing the existing YOLOv5 architecture by integrating the Convolutional Block Attention Module(CBAM)in the backbone network to boost model learning capability and Depthwise Convolutional module(DWConv)in the neck network for efficient compression of network *** Night Object Detection(NOD)and Exclusively Dark(ExDARK)dataset has been used for this *** proposed framework detects classes like humans,bicycles,and *** demonstrate that the proposed architecture achieved a higher Mean Average Precision(mAP)along with a reduction in model size and total parameters,*** proposed model is lighter by 11.24%in terms of model size and 12.38%in terms of parameters when compared to baseline YOLOv5.

关键词： Object detection deep learning nighttime road scenes YOLOv5 DWConv Zero-DCE++ CBAM

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Model for Recognition of Sign Language and Hand Gesture by Hard Hearing People

Model for Recognition of Sign Language and Hand Gesture by H...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Manjula, R. Jagadeesan, S. Ramalingam, Venkadeshan Vetriselvi, T. Johnpeter, T. Srm Institute of Science and Technology College of Engineering and Technology Department of Computing Technologies Chennai India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Tamilnadu Vellore India Information Technology Department University of Technology and Applied Sciences-Shinas Branch Sultanate of Oman Oman Vellore Institute of Technology School of Computer Science and Engineering Department of Iot Tamilnadu Vellore India K.Ramakrishnan College of Engineering Department of Computer Science and Engineering Trichy India

ISBN: (纸本)9798350313987

Hard hearing and deaf people are communicating with other people using the sign language. This is the only way of communication of such people to convey Since this is the only means by which these individuals can communicate, it is imperative that others be able to understand their language. Sign language recognition by computer begins with the acquisition of sign gestures and ends with recognition and conversion to text or voice. Static and dynamic are the two ways of sign gestures used. This study aims at focusing on the dynamic gesture recognition and the steps for recognizing sign language are discussed. The collected data are preprocessed and feature are extracted for classification. Finally the results are all investigated and developed for single user. © 2023 IEEE.

关键词： Gesture analysis Hand gesture recognition Hand tracking Sign language

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Environment Safe Biodegradable Green RFID for Green Internet of Things

Environment Safe Biodegradable Green RFID for Green Internet...

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2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Vanmathi, C. Maithili, K. School of Information Technology and Engineering Vellore Institute of Technology Tamilnadu Vellore India Department of Computer Science and Engineering KG Reddy College of Engineering and Technology Telangana Hyderabad India

ISBN: (纸本)9798350325188

The Internet of Things (IoT) platform is becoming ubiquitous since its usage is mandatory in a smart environment. IoT solutions have greatly improvised the quality of life of mankind. Internet of Things is connected with high-performance devices that need more power to provide uninterruptable services. So, energy consumption is one of the essential parameters for IoT solutions. Hence, Green IoT emerged as a solution in minimizing energy utilization of IoT devices. Usage of the Internet and applications that run on IoT is inevitable in smart cities. Even though IoT has merit and a positive impact on human life, its usage produces toxic wastes. Such an alarming constraint raised the concern for protecting the environment's nature as well as the smart cities. To mitigate the harmfulness of IoT, finding an alternate solution is mandatory, thus Green IoT has come into the picture. Green IoT is the trend with a lot of future scope for creating eco-friendly smart solutions. To minimize the toxic wastes lot of efforts are needed to reduce carbon footprint. The application of RFID in IoT smart environment is manifold. This paper proposed novel eco-friendly RFID tags. The proposed tags are designed with a water repellent to make it an eco-friendly and bio-degradable one. Moreover, this paper discusses the importance of Green IoT along with an insight into reducing carbon footprint while designing IoT devices. © 2023 IEEE.

关键词： e-waste Green IoT Green WSN Paper RFID RFID Tags

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Deepfake Video Detection Using Ada-Boosting on the DFDC Dataset

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Procedia computer science 2025年 258卷 1091-1101页

作者： Battula Thirumaleshwari Devi Rajkumar Rajasekaran Department of Computer Science and Engineering Vellore Institute of Technology Vellore 632 014 India

The rapid proliferation of deepfake videos, generated using advanced machine learning techniques to create highly realistic but misleading content, poses significant challenges across various sectors, including cybersecurity, media integrity, and personal privacy. Detecting these deepfakes has become essential for maintaining trust in digital media and preventing malicious exploitation. This paper presents a novel approach to deepfake video detection by employing the AdaBoost algorithm, a powerful ensemble learning method recognized for its ability to improve classification performance by focusing on difficult-to-classify instances. Using the Deepfake Detection Challenge (DFDC) dataset, our study demonstrates that the AdaBoost classifier, when coupled with a carefully designed feature set, achieves competitive accuracy in detecting deepfake videos. Our results show that this approach provides an effective solution for deepfake detection, with strong recall performance, making it a viable method for real-world applications.

关键词： Deepfake detection AdaBoost DFDC dataset ensemble learning machine learning video forensics

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A comprehensive Study on Security Threats in Autonomous Vehicles: Safeguarding the Future 1

A comprehensive Study on Security Threats in Autonomous Vehi...

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1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Poddar, Srisha Deepak Murali, M. Raj Asekaran, P. Prabakaran, N. School of Computer Science and Engineering Vellore Institute of Technology Vellore632014 India Sona College of Technology Department of Information Technology Salem India School of Computing SRM Institute of Science and Technology Department of Computing Technologies Chengalpattu603203 India

ISBN: (纸本)9798350361780

Autonomous vehicles, commonly referred to as self- driving vehicles, have the capability to operate and perform necessary functions without the need for human intervention. This is made possible with advanced technologies such as cameras, ultrasonic and infrared sensors, INS, GPS, DSRC, RADAR, LiDAR, and onboard computers, which work together to map the vehicle's position and surroundings. However, because these technologies are easily susceptible to manipulation, autonomous vehicles are at risk of cyber-attacks if hackers succeed to find vulnerabilities in the vehicle's system or in the electronic system of the company producing the vehicle. This study aims to investigate how machine learning and deep learning methods can be utilized to improve the defence against cyber threats aimed at autonomous vehicles by analysing several computational algorithms. © 2024 IEEE.

关键词： Autonomous vehicles

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