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检索条件"机构=Dept. Informatics Engineering of Computer Science"

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Stealth Encoding - Concealing data with the Least Significant Bit Method

Stealth Encoding - Concealing data with the Least Significan...

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Advancement in Computation & computer Technologies (InCACCT), International Conference on

作者： Bhaskar Kantapalli Siva Gnani Meruga Prasanthi Polagani Venu Babu Pingali Surya Teja Seelam dept. of computer science and engineering Seshadri Rao Gudlavalleru Engineering College Gudlavalleru INDIA dept. of computer science and engineering Seshadri Rao Gudlavalleru Engineering College Mandavalli INDIA dept. of computer science and engineering Seshadri Rao Gudlavalleru Engineering College Vijayawada INDIA dept. of computer science and engineering Seshadri Rao Gudlavalleru Engineering College Machilipatnam INDIA

Data security is a major issue for computer users these days. So, we conceal the textual data using steganography, the art and science of concealing information. In this case, we conceal both the message’s content and the fact that it is being sent. In order to make it challenging to Figure out that a hidden message is present in a specific film, this technique involves concealing secret data within a conventional, non-secret file. At its destination, the secret data is subsequently removed. Several uses of steganographic technology have distinct requirements. Here, we suggest a method for concealing data, including reverse data hiding for data extraction. The textual data will be secured in different frames of the video and audio using the Least Significant Bit (LSB) and related algorithms. We can conclude that the original data will never change the characteristics of video or audio even after applying steganography techniques.

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Autism Screening Disorder : Early Prediction 4

Autism Screening Disorder : Early Prediction

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4th IEEE International Conference on Computing, Power and Communication Technologies, GUCON 2021

作者： Rashme, Tamanna Yesmin Islam, Linta Prova, Ayesha Aziz Jahan, Sohely Uttara University Dept. of Computer Science and Engineering Dhaka Bangladesh Jagannath University Dept. of Computer Science and Engineering Dhaka Bangladesh Central Women's University Dept. of Computer Science and Engineering Dhaka Bangladesh University of Brishal Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781728199511

Autism Spectrum Disorder (ASD), which is a developmental disability due to neuro-development disorder. This disorder is a brain growth disorder that affects how an individual perceives and interacts with others, resulting in social contact and communication difficulties. Though this disorder is the result of genetic or in nature but earlier detection allows earlier intervention, which is generally more effective. Based on ASD history research, This ASD problem begins with childhood and as time goes this disorder progresses into adolescence and adulthood. Thus, in this paper, a model has been proposed for the early prediction and analysis of ASD problems in children, adolescents, and adults. Principal Component Analysis (PCA) is used for the feature reduction method and then various machine learning algorithms have been applied to three ASD datasets relating to Children, adolescents, and adults. 10-fold cross-validation is used to evaluate the performance of applied machine learning algorithms. The result produces from our proposed model shows that Logistic Regression shows outperform comparing to all other benchmark machine learning algorithms in terms of accuracy, precision, recall, and F1-score during the prediction of ASD cases. © 2021 IEEE.

关键词： Principal component analysis

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Traffic Sign Improvement using AI 5

Traffic Sign Improvement using AI

引用

5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Samantaray, Ruturaj Kumari, Ankur Swarup, Asim Swain, Jyoti Ranjan Tcs BaNCS Research Tata Consultancy Services Limited Bangalore560066 India C. V. Raman Global University Dept. of Computer Science and Engineering Bhubaneswar752054 India

ISBN: (纸本)9798350376685

Traffic signs render crucial information about the upcoming road conditions, including details on turns, intersections, and other vital features. The current traffic detection systems struggle in complex traffic scenarios such as distorted signs, poor lighting, and adverse weather conditions. Incorrect interpretation of traffic signs by automotive vehicles causes injury, property damage, and fatalities. In this study image segmentation technique is implemented to segment the images. The difficulty arises in managing uncertainty in traffic sign images caused by fading clarity conditions. Furthermore, the distinction between background and foreground becomes complex amid adverse weather or other potential factors. Rough Set based Adaptive Fuzzy C-Means (RSAFCM) technique will be implemented, and the incorporation of rough set theory will handle the uncertainty and vagueness in the image due to complex conditions. Adaptive FCM will automatically tune its parameters to better suit the image characteristics, leading to improved clustering and accuracy of the segmentation results. This combined approach will facilitate precise cleaning and segmentation of the image. By employing the solution, we aim for accurate object and background segmentation. Further, this cleaned image is fed to the CNN for feature extraction and classification. Moreover, in this study 93% accuracy is achieved by employing the proposed methodology. © 2024 IEEE.

关键词： Image segmentation

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UAV Platform for Real-time Indoor Mapping

UAV Platform for Real-time Indoor Mapping

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Innovations in High Speed Communication and Signal Processing (IHCSP), International Conference on

作者： Spruha Nitin Kshirsagar Abhishek Rawat Ravi Bhandari Dept. of Computer Science and Engineering Visvesvaraya National Institute of Technology Nagpur India Dept. of Electrical and Computer Science Engineering Institute of Infrastructure Technology Research and Management (IITRAM) Ahmedabad India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

SLAM enables Robots and UAVs to effectively understand unknown surroundings and estimate their own position within those environments. Recent advancements in 2D mapping have significantly increased its popularity for navigation tasks, primarily because it is easier to generate and requires less computational power compared to 3D mapping. This paper provides a comparison of two widely used ROS-based 2D SLAM libraries: Google Cartographer and Hector SLAM, deployed on a custom built UAV that uses 2D LiDAR, and finds Hector SLAM to be more feature rich as well as computationally efficient.

关键词： Cartography Technological innovation Simultaneous localization and mapping Laser radar Three-dimensional displays Signal processing Autonomous aerial vehicles Real-time systems Internet Robots

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Cybersecurity: Threats, Requirements, Deployments, Techniques, System Architecture, and Future Research

Cybersecurity: Threats, Requirements, Deployments, Technique...

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Smart Devices (ICSD), International Conference on

作者： Ajay Yadav Vijay Karnatak Amit Kumar Mishra Shalini Verma Pallaw Singh Aswal Neha Tripathi University Institute of Computing Chandigarh University Mohali Punjab India Dept. of Computer Science & Engineering THDC Institute of Hydropower Engineering and Technology Tehri India Dept. of Computer Science & Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science & Engineering Doon Group Of Institution Rishikesh India Computer Science and Engineering THDC Institute of Hydropower Engineering and Technology Tehri India Department of Computer Science & Engineering Graphic Era (Deemed to be University) Dehradun India

ISBN: (数字)9798350394412

ISBN: (纸本)9798350394429

Cybersecurity safeguards computer systems, net- works, and digital information from a wide variety of cyber-attacks. Its major purpose is to protect the privacy of users’ data and resources while maintaining their integrity and making them accessible in the digital world. Cybersecurity is an area that receives investment from businesses, individuals, and governments all around the world in order to secure sensitive information, key infrastructure, and digital assets. Protection of Sensitive Information Both individuals and businesses store huge amounts of sensitive information digitally, including personal data, financial records, intellectual property, and trade secrets. This information must be protected in order to prevent unauthorized access or disclosure. The protection provided by cybersecurity prevents this information from being stolen, exposed, or accessed without authorization. Cyberattacks can result in severe financial losses, including theft of funds, fraud, and the expenditures associated with recovering from a cyber incident. It is possible to prevent these types of losses by taking preventative measures. Implementing these strategies helps prevent losses of this nature. In this paper, we propose a system architecture of a generic secure system, which can be deployed for various applications, i.e., smart healthcare, Intelligent Transport Systems (ITS), smart city, etc. We discuss the threats and attacks in cyberspace. Then, we explain the requirements of cybersecurity. Further, we discuss the devastation in the absence of cyber security. We also explain the cybersecurity deployments and techniques under cybersecurity. Then, in the end, some future research directions for cybersecurity are provided.

关键词： Authorization Smart cities Systems architecture Cyberspace Loss measurement Fraud computer security Protection Smart devices Business

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Quantum transport simulation of a mid-wave infrared Ga-based type-II superlattice for curved focal-plane arrays 13

Quantum transport simulation of a mid-wave infrared Ga-based...

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Infrared Sensors, Devices, and Applications XIII 2023

作者： Glennon, John Bellotti, Enrico Div. of Material Science and Engineering Boston University BostonMA02215 United States Dept. of Electrical and Computer Engineering Boston University BostonMA02215 United States

ISBN: (纸本)9781510665880

Type-II strained-layer superlattices have theoretically predicted advantages that make them an attractive material system for use in infrared technology. However, the influence of structural disorder has been demonstrated to negatively impact the performance of these materials. A recent development in infrared technology is the use of curved focal-plane arrays which promise improved uniformity of sensitivity and a reduction in the optical complexity of the device. Curving of the wafer introduces mechanical strain into the absorber layer which can also impact the vertical carrier mobility. This work reports on an analysis of the influence of external strain and disorder on the vertical hole mobility in curved focal-plane arrays utilizing a mid-wave InAs/GaSb absorber layers. A series of quantum transport calculations is performed over a range of strain configurations for two different structures: an ideal superlattice and a superlattice with graded interfaces and disorder. Finally, the impact these effects have on the quantum efficiency of a hypothetical curved-FPA is approximated, and a potential avenue for improved device design is suggested. © 2023 SPIE. All rights reserved.

关键词： Quantum efficiency

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On a Piecewise Linear Function Approximation for Quantum Computation

On a Piecewise Linear Function Approximation for Quantum Com...

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IEEE Asia-Pacific Conference on Circuits and Systems

作者： Hideaki Okazaki Dept. of Informatics Faculty of Informatics Computer Science Course (or Field) Graduate school of Electrical and Information Engineering Shonan Institute of Technology Fujisawa Kanagawa Japan

In quantum computing, although correspondences between quantum circuits and polynomials have been explored, a correspondence between a quantum circuit and a low-degree polynomial, even a linear function has not fully clarified. In this paper, a quantum circuit approximating a linear function for building piecewise linear functions, is proposed. First, for qubit operations, a dimensionless form in $[0,1]$ of a linear function is provided. Secondly, a theorem of a parameterized quantum circuit approximating linear functions are provided. Thirdly, by using Yao: an open source Julia package, the quantum circuit simulation results to approximate the linear functions are presented. Finally, the principal results are summarized.

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Credit Card Fraud Detection Utilizing Machine Learning, Deep Learning, and Bayesian Networks

Credit Card Fraud Detection Utilizing Machine Learning, Deep...

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2025 International Conference on Artificial Intelligence and Data engineering, AIDE 2025

作者： Reddy, Arra Keerthan Vardhan Rapeti, Harsha Reddy, Doduguru Amarnath Srinivas, M. Computer Science Engineering Amrita Vishwa Vidyapeetham Amaravati India Amrita Vishwa Vidyapeetham Dept. of Computer Science Amaravati India

ISBN: (纸本)9798331527518

Detecting credit card fraud is a critical challenge in the modern financial landscape, where robust and efficient solutions are essential to mitigate losses and safeguard consumers. This research evaluates and contrasts three primary approaches to this issue: Machine Learning (ML), Deep Learning (DL), and Bayesian Networks (BN). Using publicly available datasets, several ML techniques, including Logistic Regression, Support Vector Machines (SVM), Random Forests, Decision Trees, and XGBoost, were assessed alongside DL models such as Multi-Layer Perceptron (MLP) and Deep Neural Networks (DNNs). Additionally, a Bayesian Network was employed to analyze causal relationships between fraud-related features, offering an interpretable probabilistic framework. While ML and DL approaches achieved high performance regarding accuracy, precision, and recall, the Bayesian Network distinguished itself by providing enhanced interpretability and reliability. This study highlights the complementary strengths of these methods and the potential for their integration to develop adaptive and efficient fraud detection systems © 2025 IEEE.

关键词： Deep neural networks

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Investigating the Effectiveness of Algorithms in Serverless Cloud Computing

Investigating the Effectiveness of Algorithms in Serverless ...

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2024 International Conference on E-Mobility, Power Control and Smart Systems, ICEMPS 2024

作者： Jindal, Mayank Mimani, Sushant Pillai, Saniaikanth E Vadakkethil Somanathan Jiwani, Nasmin Kiruthiga, T. San Jose CA United States GoDaddy WA United States School of Electrical Engineering and Computer Science University of North Dakota Grand Forks United States University of The Cumberlands Dept. of Information Technology WilliamsburgKY United States Vetri Vinayaha College of Engineering and Technology Dept. of Ece Trichy India

ISBN: (纸本)9798350394399

Server less cloud computing has won vast recognition in current years because of its capability to provide green and price-effective solutions for numerous computing needs. On this model, cloud carriers manage the scaling and provisioning of sources, permitting users to consciousness totally on their application code. As an end result, server less computing gives an appealing opportunity to standard server-primarily based computing, in which customers needed to control the whole infrastructure themselves. However, with the rise of server less computing, there was a growing challenge about the effectiveness of algorithms on these surroundings. Traditional algorithms are designed to run on a set of resources, which may not translate properly to the dynamic and elastic nature of server less structures. This has caused a want for studies in developing algorithms mainly tailor-made for server less computing. One fundamental assignment on this place is the optimization of resources and price inside the server less environment. Since customers simplest pay for the resources they use, it's far critical to layout algorithms that minimize useful resource consumption and value. Previous research has already indicated that conventional algorithms that rely upon pre-determined aid allocation won't carry out nicely within the server less model. Moreover, server less systems additionally introduce new challenges in phrases of useful resource availability, network latency, and processing delays due to the pay-in keeping with-use model. © 2024 IEEE.

关键词： Cloud computing

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An Adaptive Traffic Signal Management System Incorporating Reinforcement Learning

An Adaptive Traffic Signal Management System Incorporating R...

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2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems, AICERA/ICIS 2023

作者： Masfequier Rahman Swapno, S.M. Chhabra, Gunjan Kaushik, Keshav Nobel, S.M. Nuruzzaman Islam, Md Babul Shahiduzzaman, Md. Bangladesh University of Business and Technology Dept of Computer Science and Engineering Dhaka Bangladesh Graphic Era Hill University Dehradun India University of Petroleum and Energy Studies Dehradun India Unical Dept. of Computer Modeling Electronic and System Engineering Rende Italy

ISBN: (纸本)9798350303452

Although there has been a lot of study done on traffic monitoring, control, modeling, and the use of artificial intelligence to handle these problems, there are still a lot of critical problems that need to be solved. In existing traffic signal research, a major problem is longer delays and queues, causing significant traffic congestion. Our research aims to minimize these issues, successfully reducing delays and queues. This has led to improved traffic flow and a significant decrease in congestion. Reinforcement learning for traffic signal control uses RL techniques to create intelligent agents that make real-time decisions about changing traffic signals at intersections. Its goal is to enhance traffic flow, reduce congestion, and improve overall transportation efficiency by optimizing signal phases dynamically. Reinforcement property state contain the current circumstances at an intersection, actions correspond to the choices made by the traffic signal controller, and rewards reflect the controller's performance and are in line with traffic objectives like minimizing delays, reducing congestion, cutting down on travel times, and improving safety. We scrutinize Deep Q learning by reason of Deep Q Learning gives Q value, By estimating and updating Q-values, the agent can learn which actions are more beneficial in different traffic conditions. We are able to cut the queue length by 9.7% to shorten wait times and relieve traffic. Get a happy outcome of traffic signal control and improve rewards by 9.7%. This research study showcases the promising capabilities of reinforcement learning in enhancing the synchronization of traffic light controllers, effectively mitigating the adverse consequences of traffic congestion within urban environments. The adoption of this innovative approach holds the promise of fostering a more sustainable and streamlined transportation system for the future. © 2023 IEEE.

关键词： Reinforcement learning

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