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Innovation in technology (ASIANCON), Asian Conference on

作者： Delano Oscar Do Rosario Lourenco M V H Sai Sriraj Krotha Kayne Thambi Vibho Ranjan School of Computer Science & Engineering Vellore Institute of Technology Chennai Tamil Nadu India

Attackers commonly employ malicious URLs and harmful QR codes to spread malware and phishing scams; therefore, they need to be classified. Malicious URLs that lead visitors to phishing or malware-infected websites that steal personal data can be included in emails, social media posts, or website content. Rogue QR codes can be used to propagate malware, steal data, or direct users there, much like malicious websites can. In this paper, the classification of malicious content is provided using two approaches:(1) Malware URL categorization based on ML and malware QR codes (2) classification of malicious QR codes based on deep learning. The first technique classifies URLs as dangerous or benign using machine learning models that are trained on features derived from the URLs. Several ML techniques, such as Random Forest, Naive Bayes, and Support Vector Machine, are used to evaluate the model's performance. The second option focuses on classifying harmful QR codes using deep learning techniques. The classification of QR codes as malicious or benign is done using CNN and well-known transfer learning models like RESNET. In general, the offered approaches provide effective techniques to classify unsafe material, which can be utilized to enhance security.

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FEAT: Towards Fast Environment-Adaptive Task Offloading and Power Allocation in MEC 42

FEAT: Towards Fast Environment-Adaptive Task Offloading and ...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Ren, Tao Hu, Zheyuan He, Hang Niu, Jianwei Liu, Xuefeng Institute of Software Chinese Academy of Sciences Laboratory for Internet Software Technologies Beijing China Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing China Zhongguancun Laboratory Beijing China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China

ISBN: (纸本)9798350334142

Mobile edge computing (MEC) has been proposed to provide mobile devices with both satisfactory computing resources and latency. Key issues in MEC include task offloading and power allocation (TOPA), for which deep reinforcement learning (DRL) is becoming a popular methodology. However, most DRL-based TOPA approaches are typically developed in a certain environment, lacking the adaptability to unseen environments. Motivated by this, this paper proposes a Fast Environment-Adaptive TOPA (FEAT) approach that could adapt to unseen environments with little fine-tuning. Specifically, we first split MEC states into the internal state and environmental state. Then, based on these two types of states, we develop two main components of FEAT - a group of internal state-dependent TOPA meta-policies and an environmental state-embedded steerer. Meta-policies learn TOPA skills within the internal state space (allowing reusing meta-policies in different environments), while the steerer learns to choose appropriate meta-policies according to embedded environmental states. When encountering an unseen environment with the same internal state space, FEAT only needs to fine-tune the steerer using the newly embedded environmental state with few internal state explorations. Extensive experimental results on simulation and testbed demonstrate that FEAT outperforms the state-of-the-art by more than 16.4% in terms of fine-tuning speeds. © 2023 IEEE.

关键词： Mobile edge computing

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Multi-stage biomedical feature selection extraction algorithm for cancer detection

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SN Applied sciences 2023年第5期5卷 131页

作者： Keshta, Ismail Deshpande, Pallavi Sagar Shabaz, Mohammad Soni, Mukesh Bhadla, Mohit kumar Muhammed, Yasser Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia College of Engineering Pune India Arba Minch University Arba Minch Ethiopia Department of CSE University Centre for Research & Development Chandigarh University Punjab Mohali140413 India Department of Information Technology Ahmedabad Institute of Technology Ahmedabad India College of Technical Engineering Al-Farahidi University Baghdad Iraq

Cancer is a significant cause of death worldwide. Early cancer detection is greatly aided by machine learning and artificial intelligence (AI) to gene microarray data sets (microarray data). Despite this, there is a significant discrepancy between the number of gene features in the microarray data set and the number of samples. Because of this, it is crucial to identify markers for gene array data. Existing feature selection algorithms, however, generally use long-standing, are limited to single-condition feature selection and rarely take feature extraction into account. This work proposes a Multi-stage algorithm for Biomedical Deep Feature Selection (MBDFS) to address this issue. In the first, three feature selection techniques are combined for thorough feature selection, and feature subsets are obtained;in the second, an unsupervised neural network is used to create the best representation of the feature subset to enhance final classification accuracy. Using a variety of metrics, including a comparison of classification results before and after feature selection and the performance of alternative feature selection methods, we evaluate MBDFS's efficacy. The experiments demonstrate that although MBDFS uses fewer features, classification accuracy is either unchanged or enhanced. © 2023, The Author(s).

关键词： Feature Selection

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IoT-Embedded Traffic Cones with CNN-based Object Detection to Roadwork Safety 2

IoT-Embedded Traffic Cones with CNN-based Object Detection t...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Saillaja, V. Rehaman Pasha, Mohammed Krishnaveni, S. Ravinder, Bathini Srinivasan, S. Nandagopal, V. SRM Institute of Science and Technology Kattankulathur Faculty of Management Studies Tamil Nadu Chennai India Malla Reddy Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India S.A.Engineering College Department of Information Technology Tamil Nadu Chennai India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India School of Engineering Mohan Babu University Department of Electrical and Electornics Engineering Andrapradesh Tirupathi India

ISBN: (纸本)9798350327533

Motorists and construction workers both need to use extreme caution while passing through roadwork zones. This research provides a novel strategy for increasing the security of roadwork by combining Internet of Things (IoT) devices with object recognition through Convolutional Neural Networks (CNNs). To improve communication and data sharing with a centralized control center, this technology equips conventional traffic cones with integrated IoT devices. Traffic cones with IoT sensors track things like their location, the brightness of the surrounding area, and the volume of vehicles on the road. Useful information about the roadwork environment is supplied wirelessly from the cones to the control center. Live video feeds from the IoT-enabled traffic cones are analyzed using a CNN-based object identification algorithm. The model can view and follow vehicles, pedestrians, and other possible threats in a road construction zone. Construction workers and traffic management officials are immediately notified of potential safety hazards through real-time object detection warnings. In terms of roadwork safety, this integrated system has several advantages, such as enhanced knowledge of the current situation, faster identification of potential dangers, and more effective traffic management. Roadwork zones may be made safer for employees and the public using IoT technology and cutting-edge computer vision methods. © 2024 IEEE.

关键词： Hazard Detection Object Detection Roadwork Environment Safety Alerts Traffic Cones

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Enhancing Drone Surveillance with NeRF: Real-World Applications and Simulated Environments 43

Enhancing Drone Surveillance with NeRF: Real-World Applicati...

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43rd AIAA DATC/IEEE Digital Avionics systems Conference, DASC 2024

作者： Lindén, Joakim Burresi, Giovanni Forsberg, Håkan Daneshtalab, Masoud Söderquis, Ingemar School of Innovation Design and Engineering Mälardalen University Division of Intelligent Future Technologies Västerås Sweden Saab Aeronautics Stockholm Sweden School of Electrical Engineering and Computer Science Royal Institute of Technology Division of Electronics and Embedded Systems Stockholm Sweden

ISBN: (纸本)9798350349610

Machine Learning (ML) systems require representative and diverse datasets to accurately learn the objective task. In supervised learning data needs to be accurately annotated, which is an expensive and error-prone process. We present a method for generating synthetic data tailored to the use-case achieving excellent performance in a real-world usecase. We provide a method for producing automatically annotated synthetic visual data of multi rotor unmanned aerial vehicles (UAV) and other airborne objects in a simulated environment with a high degree of scene diversity, from collection of 3D models to generation of annotated synthetic datasets (synthsets). In our data generation framework SynRender we introduce a novel method of using Neural Radiance Field (NeRF) methods to capture photo-realistic high-fidelity 3D-models of multirotor UAVs in order to automate data generation for an object detection task in diverse environments. By producing data tailored to the real-world setting, our NeRF-derived results show an advantage over generic 3D asset collection-based methods where the domain gap between the simulated and real-world is unacceptably large. In the spirit of keeping research open and accessible to the research community we release our dataset VISER DroneDiversity used in this project, where visual images, annotated boxes, instance segmentation and depth maps are all generated for each image sample. © 2024 IEEE.

关键词： Drones

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Mantaray Foraging Optimization based Makespan Enhancement in Cloud based Scheduling Environment 1

Mantaray Foraging Optimization based Makespan Enhancement in...

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1st IEEE International Conference on Automation, Computing and Renewable systems, ICACRS 2022

作者： Aswini, J. John Augustine, P. Chithra, S.M. Johny Elma, K. Kopperundevi, N. Parasuraman, T. Affiliated to Anna University India Department of Information Technology Sri Eshwar College of Engineering Kondampatti-Post Vadasithur-Via Kinathukadavu Coimbatore India Department of Mathematics R. M. K. College of Engineering and Technology India Information Technology Easwari Engineering College India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India School of Physical Education & Sports Sciences Hindustan Institute of Technology & Science Tamil Nadu Chennai India

ISBN: (纸本)9781665460842

Cloud computing (CC) delivers on-demand and powerful business environments. It can be constructed on top of virtualized data hubs. Virtualization renders a flexible infrastructure for cloud. However, scheduling the tasks and managing the resources in virtualized data hubs will be difficult. Maximizing the virtual machine utilization and minimizing the total makespan is imperative in the cloud environment. The task scheduling issue can be indicated as multi-objective optimization problem, which is solved by utilizing optimization methods. This study develops a Mantaray Foraging Optimization based Makespan Enhancement in Cloud based Scheduling Environment (MROME-CSE) technique. The presented MROME-CSE technique aims to allocate the resources proficiently in the cloud environment. In the presented MROME-CSE technique, the behavior of manta rays is mainly involved. Furthermore, the MROME-CSE method derived an objective function to allocate n individual tasks to m heterogeneous accessible resources, thereby task conclusion period was minimalized and resource was fully exploited. The experimental evaluation of the MROME-CSE technique is examined briefly. The experimental outcomes stated the superior performance of the MROME-CSE technique over other recent models. © 2022 IEEE

关键词： Cloud computing

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Detecting Uteropelvic Junction Obstruction at an Early Stage using Ultrasound Images through an Interplanetary File System

Detecting Uteropelvic Junction Obstruction at an Early Stage...

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Data Intelligence and Cognitive Informatics (ICDICI), International Conference on

作者： Kaviya. R Mridula Prasad S. Pavithra B. Suresh Chander Kapali School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Biomedical Engineering Easwari Engineering College Chennai India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

Ureteropelvic junction obstruction (UPJO) is a medical condition characterized by a blockage at the junction between the ureter and the kidney, leading to impaired urine flow and increased fluid pressure. If left untreated, UPJO can progress to hydronephrosis and potentially result in irreversible kidney damage. This paper presents a novel system for the early detection and severity grading of UPJO using Deep Learning computer-Aided Diagnosis (DL-CAD) techniques. The proposed approach leverages ultrasound imaging as a primary diagnostic tool, noted for its cost-effectiveness, absence of radiation, and non-invasive nature. To enhance the security and integrity of medical data transmission, we integrate blockchain technology into our framework. Our solution combines a DL- CAD model featuring an attention-based pyramid semantic segmentation network with a discrete wavelet transformation- processed residual classification network. An IoT device is employed to detect patient abnormalities and transmit this data to the diagnosis model via an InterPlanetary File System (IPFS). This decentralized storage solution facilitates secure data management across a peer-to-peer network. Ultimately, the proposed framework not only identifies the presence of UPJO but also assesses its severity, ensuring that patient data remains protected throughout the diagnostic process.

关键词： Solid modeling Ultrasonic imaging Medical conditions Semantic segmentation InterPlanetary File System Security Junctions Kidney Secure storage Medical diagnostic imaging

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Multi-Positive Sample Quantum Contrastive Learning for Human Activity Recognition

Multi-Positive Sample Quantum Contrastive Learning for Human...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Ren, Yanhui Wang, Di An, Lingling Mao, Shiwen Wang, Xuyu Xidian University Guangzhou Institute of Technology Guangzhou China Xidian University School of Computer Science and Technology Xi'an China Auburn University Department of Electrical and Computer Engineering AuburnAL United States Florida International University Knight Foundation School of Computing & Information Sciences MiamiFL United States

ISBN: (纸本)9798350351255

Human activity recognition (HAR) based on wearable devices has become an active research direction in the field of ubiquitous computing, and has a wide range of Internet of Things (IoT) applications. Unfortunately, it is challenging to obtain large amounts of labeled sensing data, and manual annotation is time-consuming and labor-intensive, making it impossible for the extensive deployment of HAR systems. Consequently, self-supervised learning has emerged to address this challenge by training on unlabeled data. However, traditional contrastive learning fails to simulate more sample diversity problems caused by environmental heterogeneity and sensor heterogeneity. In this paper, we propose a multi-positive sample quantum contrastive learning (MPSQCL) framework. By increasing the positive samples for contrastive learning and leveraging the advantages of quantum machine learning (QML) techniques, the richer features of input samples are extracted to improve the robustness and generalization of the model. Moreover, we design a new contrastive loss function to adapt to multiple positive sample contrastive learning scenarios. Finally, we validate the effectiveness of the proposed framework on several publicly available HAR datasets. © 2024 IEEE.

关键词： Contrastive Learning

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Facial Landmarks Based Drowsy Driver Detection Model with OpenCV and DLib

Facial Landmarks Based Drowsy Driver Detection Model with Op...

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Emerging Research in Computational science (ICERCS), International Conference on

作者： Karthikeyan N Mansoor Hussain D M. Rishit Varma A. A. Shaheena Arun Am Aaditya Jain School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Tamilnadu India

In the present world, a driver falling asleep while driving is one of the major causes of accidents. It is highly dangerous for the driver as well as the others passing by the vehicle who are prone to accidents. It is possible to detect the early signs of drowsiness, which helps the driver become conscious and not lose control of the vehicle. This paper presents a facial landmark-based drowsy driver detection model using computer vision. It uses the OpenCV module to detect the drowsiness level of a driver. Further DLib modules are used to extract the facial landmarks, process the required eye and mouth parameters, and alert the driver with an alarm. This research consists of three parts. Initially, it captures the live video and feeds it to the computer, where a pre-trained model is used to get the facial landmarks. Secondly, the pre-trained facial landmarks are processed using a machine learning module to calculate the required EAR and MAR ratios. Finally, the alarm system is enabled after finding the state of the driver using the calculated metrics.

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Smart Shuttle Based Transformation system for Smart Cities 6

Smart Shuttle Based Transformation system for Smart Cities

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6th IEEE International Conference on Signal Processing, Computing and Control, ISPCC 2021

作者： Araujo, Neil Francisco Rajasekaran, Rajkumar Singh, Vijendra Vellore Institute Of Technology School Of Computer Science And Engineering Vellore India University Of Petroleum And Energy Studies School Of Computer Science Dehradun India

ISBN: (纸本)9781665425520

The main aim of the paper is to develop a shared shuttle-based system in order to reduce congestion in cities and thereby reduce carbon dioxide emissions. There are multiple modules in this system but to summarize there exist two databases one containing user pickup and drop-off points, the other containing the available drivers and their current positions. The map is partitioned into hexagons in order to match users and drivers. 3 people can share a single car along with cargo thus reducing the footprint of overall passenger and cargo vehicles in cities. The system proposed is based on real-time foot traffic in the city. This data includes most busy times for particular restaurants, offices, etc. For example, if a 500 person office building finishes its workday at 5 pm then there will be a spike in taxi requests, this is preemptively known by the system and a fixed shuttle point is generated near the office with cars in waiting thus reduces the travel distance and fuel consumed by an individual taxi. This system will be the most effective when 100% of all vehicles in the city are used for shared transportation and travel the routes set by the system © 2021 IEEE.

关键词： Carbon dioxide

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