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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Rakib Hossain Rifat Sunzida Siddique Laxmi Rani Das Mohd Ariful Haque Dept. of Computer Science and Engineering BRAC University Mohakhali Dhaka Bangladesh Dept. of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Dept. of Computer Science and Telecommunication Engineering Noakhali Science and Technology University Sonapur Bangladesh Dept. of Cyber-Physical Systems Clark Atlanta University Atlanta Georgia

Eyeglasses are not only used to protect our vision and prevent dust from getting into our eyes. Additionally, glass that fits properly can give a person an elegant appearance. However, people often find it difficult to choose eyeglasses that fit their face shape; to address this issue, we have proposed a novel architecture in this paper. In order to do this, we created a pipeline that can recommend eyeglasses based on the form of the eyes using multiple transfer learning architecture to predict the face shape from a given image. We utilized InceptionV4 [17], InceptionV3[18], Vit Small [12], DenseNet121 [10], ResNet50 [9], and VGG16 [16] to predict the facial shape from the image and achieve a test accuracy of 75%. We used 5500 photos with five different face shapes (Heart, Oblong, Oval, Round, Square) for this experiment, and two distinct datasets were gathered from Kaggle [2] and GitHub [1]. By simply uploading the photograph to our recommendation system, our proposed solution can assist users in selecting the appropriate eyewear.

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Fuzzy Based Intelligent Transportation Systems for Smart Cities to Mitigate Road Traffic Congestion

Fuzzy Based Intelligent Transportation Systems for Smart Cit...

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International Conference on Innovations in Science, Engineering and technology (ICISET)

作者： Md. Humayun Kabir Md Saiful Islam Md Jiabul Hoque Dept. of Electronics and Telecommunication Engineering Chittagong University of Engineering and Technology Chittagong Bangladesh Dept. of Computer and Communication Engineering International Islamic University Chittagong Chittagong Bangladesh

ISBN: (数字)9798350355499

ISBN: (纸本)9798350355505

Traffic congestion in smart cities is a growing concern. This paper explores the application of Fuzzy Logic in Intelligent Transportation Systems (ITS) to address this challenge. Fuzzy logic can handle imprecise and dynamic traffic data and offers advantages over traditional fixed-time signal control. The research proposes a Fuzzy-based ITS framework for dynamically traffic signal control. This framework utilizes real-time sensor traffic data to adjust signal timings, dynamically prioritizing congested areas. The system also incorporates emergency vehicle detection using siren sensors. This ensures priority for emergency vehicles by granting them the right of way. This paper discusses the design of the fuzzy inference system, including the selection of fuzzy variables and membership functions. The effectiveness of the proposed system is evaluated through simulations, demonstrating its potential to reduce traffic congestion and improve traffic flow in smart cities.

关键词： Fuzzy logic Transpiration Technological innovation Uncertainty Smart cities Computational modeling Vehicle detection Transportation Vehicle dynamics Traffic congestion

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Optimizing IoT Security:Advanced Intrusion Detection for IoT Networks Using Deep Learning Techniques

Optimizing IoT Security:Advanced Intrusion Detection for IoT...

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Intelligent Technologies (ACOIT), Asian Conference on

作者： Khandakar Rabbi Ahmed Nusrat Ameri Jannat Rosul Nisha Aminul Hoque Towfika Salam Md Istiak Hasan Rial Miyan Research Institute International University of Business Agriculture and Technology Uttara Model Town Dhaka Bangladesh Dept. of Mechatronics Engineering Rajshahi University of Engineering and Technology (RUET) Kazla Rajshahi Bangladesh Physics Discipline Science Engineering and Technology School Khulna University Khulna Bangladesh Dept. of Computer Science and Engineering Jahangirnagar University (JU) Savar Dhaka Bangladesh Dept. of Computer Science and Engineering Bangabandhu Road Tongabari Asian University of Bangladesh Ashulia Dhaka Dept. of Computer Science and Engineering Rajshahi University of Engineering and Technology (RUET) Kazla Rajshahi Bangladesh Dept. of Computer Science and Engineering Aditya College of Engineering (AOCE) Surampelam Kakinada Andhra Pradesh India

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

The advancement of Internet of Things (IoT) devices has ushered in a new era of connectivity and convenience. Intrusion Detection Systems (IDS) are vital for protecting IoT networks by identifying abnormal activities indicative of malicious intent. Network systems face Distributed Denial of Service (DDoS), Denial of Service (DoS), and service scans, requiring sophisticated IDS that can quickly detect and neutralize these threats. Current IDS solutions need accuracy, efficiency, flexibility, and scalability improvements to detect attack traffic across various IoT networks effectively. The evolving nature of these attacks necessitates the use of up-to-date datasets. This paper contributes to IoT security by developing and assessing an IDS using the BoTNeTIoT-L01 dataset under Mirai and Gafgyt botnet attacks and different sub-type attacks. We evaluate several machine learning models: Decision Trees (DT), Random Forests (RF), LightGBM, and Multilayer Perceptron (MLP). Our results show that MLP outperforms other models, achieving a precision of $\mathbf{9 6. 4 8 \%}$, recall of $\mathbf{9 6. 2 4 \%}$, $\mathbf{F}$-score of $\mathbf{9 6. 7 2 \%}$, and accuracy of $\mathbf{9 6. 6 5 \%}$. This approach ensures our defence strategies remain effective and responsive to the latest attack vectors.

关键词： Radio frequency Deep learning Technological innovation Accuracy Biological system modeling Scalability Intrusion detection Vectors Internet of Things computer crime

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Immersive Viewing of Dento-facial Medical Data in Virtual Reality 14

Immersive Viewing of Dento-facial Medical Data in Virtual Re...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Bhat, Shreenidhi H. Hareesh, K.S. Kamath, Abhay T. Kudva, Adarsh Manipal Institute of Technology Manipal Academy of Higher Education Dept. Data Science and Computer Applications Manipal India Consultant-Oral and Maxillofacial Surgery Kmc Hospitals Mangaluru India Manipal College of Dental Sciences Manipal Academy of Higher Education Dept. Oral and Maxillofacial Surgery Manipal India

ISBN: (纸本)9798350335095

Virtual reality (VR) requires dynamic and responsive rendering technologies to offer an immersive user experience. Failing, users may experience motion sickness due to motion lag in virtual space. This work aims to create a framework with a higher framerate to decrease the motion sickness in turn increasing the virtual world viewing quality. Essentially, an interactive environment is developed between existing volume rendering engines OpenGL and GPU-based ray casting techniques for rendering the dentofacial data volume. The developed visualization methods are incorporated into Head Mount Device(HMD) to offer immersive effects. The performance of the VR rendering application measured in terms of framerate was observed to be 125 frames per second, significantly higher than the current HMDs framerate. In addition, the adaptation of rendered image quality is activated by adjusting the sampling distance and jitter to complete the rendering process in the allotted time. The achieved result is expected to offer satisfactory comfort to the viewer with reduced motion sickness, particularly in pre-surgical planning for dentofacial surgery. © 2023 IEEE.

关键词： Virtual reality

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A color image watermarking in the frequency domain using a teaching-learning optimization algorithm 6

A color image watermarking in the frequency domain using a t...

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6th International Conference on Image Information Processing, ICIIP 2021

作者： Badoga, Nikhlesh Kumar Goyal, Raman Kumar Mehta, Rajesh Thapar Institute of Engineering Technology Dept. Computer Science Engnieering Patiala India

ISBN: (纸本)9781665433617

This paper presents a color image watermark technique that employs teaching learning-based optimization algorithm (TLBO) and lagrangian twin support vector regression (LTSVR) in the frequency domain. By analyzing the statistical property of the selected wavelet band (LL sub-band) after single-level decomposition, LTSVR is used for extraction of watermark and embedding processes. TLBO is used to find the optimal value of watermark strength for different selected blocks of the image in the wavelet domain. Various kinds of images are considered to test the imperceptibility and robustness of the watermark in experimental results. The metric Peak Signal to Noise Ratio (PSNR) has been used for watermark images to evaluate the: (i) imperceptibility, (ii) quality. Bit error rate (BER) and normalized correlation (NC) value is computed to determine the effectiveness and standards of the extracted watermark. JPEG compression attack with different quality factors (QF) ranging from 10 to 90 is evaluated using robustness to determine the proficiency of the proposed work. Experimental results show that the proposed method is robust to JPEG compression as compared to state of art method. © 2021 IEEE.

关键词： Color

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Meditation for Happy and Healthy Life: A Quantitative Assessment using Heart Rate Variability using ECG 4

Meditation for Happy and Healthy Life: A Quantitative Assess...

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4th International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2022

作者： Sharma, Akhilesh Kumar Tiwari, Shamik Srivastava, Devesh Kumar Choudhary, Deevesh Vishwakarma, Santosh K Information Technology Manipal University Jaipur Jaipur India School of Computer Science Dehradun India Manipal University Jaipur Dept. of Cse Jaipur India

ISBN: (数字)9781665462464

ISBN: (纸本)9781665462464

The pursuit of happiness is the ultimate purpose of human life in modern civilization. Happiness is defined in Indian culture as a mood or quality that occurs at birth in a person and distinguishes it from other species. Meditation, as the phrase from the Ashtavakra Gita suggests, is a spiritual instrument for increasing happiness in life. Meditation is a term that refers to a set of techniques that are often used to relieve stress and increase overall well-being. These days, HRV (heart rate variability) appears to be on each person mind, at least among those who want to track their own or their customers' sleep, recuperation, performance, or inclusive mental health. The findings of this study are both inspiring and useful, as the current analysis clearly reveals that meditation has a considerable impact on heart rate dynamics. The results of heart rate variability metrics provide a quantifiable measurement of this. It plainly states that meditation improves one's health, which leads to happiness as the ultimate objective. In this research article the ECG based health analysis and happiness measurement demonstrated which help oneself to get a maximum amount of happiness through the use of of yoga and natural resources. © 2022 IEEE.

关键词： Electrocardiograms

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Smart CropGuard: Innovating Disease Detection with Inception CNN and LSTM Peephole Networks

Smart CropGuard: Innovating Disease Detection with Inception...

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Telecommunications Conference (ITC-Egypt), International

作者： Menna Ahmed Hamed Ahmed Mostafa Walid Mohamed Kazem Maryam El Gohary Shahd Gawad Mohamed Ibrahim Ahmed Ashraf Mai Ramadan Ibraheem Artificial intelligence engineering Dept. Faculty of Computer Engineering Galala University Egypt Head of Information Technology Dept. Faculty of Computers and Information Kafrelsheiksh University Egypt

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

Detecting plant leaf diseases is a novel approach for automated plant leaf disease detection, integrating the Inception Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM) networks enhanced with peephole connections. By synergistically leveraging these advanced deep learning architectures, our research aims to develop a comprehensive framework for precise and efficient disease detection, addressing challenges in traditional methods and advancing agricultural sustainability.

关键词： Measurement Training Plant diseases Accuracy Predictive models Convolutional neural networks Reliability

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Strategies for Request-Response Logging in Microservices Architecture 20

Strategies for Request-Response Logging in Microservices Arc...

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20th IEEE Jubilee International Symposium on Intelligent Systems and Informatics, SISY 2022

作者： Basciftci, Fatih Aydemir, Fikri Selcuk University Faculty of Technology Dept. of Computer Engineering Konya Turkey Information Technologies Kuveyt Türk Bank RandD Center Istanbul Turkey

ISBN: (纸本)9781665489881

Microservices Architecture is the prevailing architectural choice today for building distributed software systems in various business sectors, such as telecommunications, e-commerce, and finance. It is often necessary to log the full content of request and response messages (i.e., the entire body of these messages) that are entering and leaving such distributed systems due to legal book-keeping requirements. In this work, two systematic design strategies were used to realize the structured logging of request-response messages including their entire message body, particularly in a microservices architecture-based distributed software system. As a case study, a prototype per strategy was implemented and deployed into an existing Microservices Architecture-based banking system, which was provided by a commercial bank for the research presented in this paper in the Kubernetes cluster. Load tests were performed against this banking system to measure average request throughputs and average response times per logging strategy for analysis purposes. The results that are presented in this paper are expected to be helpful for both researchers and practitioners in the software industry who need a similar logging solution. © 2022 IEEE.

关键词： Load testing

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Learning Bayes-Optimal Channel Estimation for Holographic MIMO in Unknown EM Environments

Learning Bayes-Optimal Channel Estimation for Holographic MI...

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IEEE International Conference on Communications (ICC)

作者： Wentao Yu Hengtao He Xianghao Yu Shenghui Song Jun Zhang Ross D. Murch Khaled B. Letaief Dept. of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Dept. of Electrical Engineering City University of Hong Kong Hong Kong

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Holographic MIMO (HMIMO) has recently been recognized as a promising enabler for future 6G systems through the use of an ultra-massive number of antennas in a compact space to exploit the propagation characteristics of the electromagnetic (EM) channel. Nevertheless, the promised gain of HMIMO could not be fully unleashed without an efficient means to estimate the high-dimensional channel. Bayes-optimal estimators typically necessitate either a large volume of supervised training samples or a priori knowledge of the true channel distribution, which could hardly be available in practice due to the enormous system scale and the complicated EM environments. It is thus important to design a Bayes-optimal estimator for the HMIMO channels in arbitrary and unknown EM environments, free of any supervision or priors. This work proposes a self-supervised minimum mean-square-error (MMSE) channel estimation algorithm based on powerful machine learning tools, i.e., score matching and principal component analysis. The training stage requires only the pilot signals, without knowing the spatial correlation, the ground-truth channels, or the received signal-to-noise-ratio. Simulation results will show that, even being totally self-supervised, the proposed algorithm can still approach the performance of the oracle MMSE method with an extremely low complexity, making it a competitive candidate in practice.

关键词： Training 6G mobile communication Machine learning algorithms Simulation Channel estimation Estimation Machine learning

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A centrality measure for quantifying spread on weighted, directed networks

arXiv

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arXiv 2023年

作者： Fink, Christian G. Fullin, Kelly Gutierrez, Guillermo Omodt, Nathan Zinnecker, Sydney Sprint, Gina McCulloch, Sean Dept. of Physics Gonzaga University United States Dept. of Computer Science Ashland University United States Dept. of Industrial and Systems Engineering Georgia Institute of Technology United States Dept. of Mechanical Engineering Gonzaga University United States Dept. of Computer Science Gonzaga University United States Dept. of Computer Science Ohio Wesleyan University United States

While many centrality measures for complex networks have been proposed, relatively few have been developed specifically for weighted, directed (WD) networks. Here we propose a centrality measure for spread (of information, pathogens, etc.) through WD networks based on the independent cascade model (ICM). While deriving exact results for the ICM requires Monte Carlo simulations, we show that our centrality measure (Viral Centrality) provides excellent approximation to ICM results for networks in which the weighted strength of cycles is not too large. We show this can be quantified with the leading eigenvalue of the weighted adjacency matrix, and we show that Viral Centrality outperforms other common centrality measures in both simulated and empirical WD networks. Copyright © 2023, The Authors. All rights reserved.

关键词： Complex networks

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