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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Arti Badhoutiya Borra Sivaiah Muntather Almusawi Rajan Verma Rajesh Singh Sanjeevkumar Angadi Department of Electrical Engineering GLA University Mathura Department of CSE CMR College of Engineering & Technology The Islamic university Najaf Iraq Chitkara Center for Research and Development Chitkara University Himachal Pradesh India Department of Electronics & Communication engineering Uttaranchal Institute of Technology Uttaranchal University Dehradun India Computer Science and Engineering Nutan College of Engineering and Research Talegaon Dabhade Pune Maharashtra India

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

This article introduces a Generative Adversarial Network tailored for medical imaging, in particular designed to synthesize retinal images alongside side their segmented mask. This innovation addresses the challenge of obtaining big datasets for deep getting to know, in particular in scientific fields wherein expert annotations are high priced and time-ingesting. It not satisfactory generates fantastic synthetic scientific pics for supervised analysis but moreover achieves cutting-edge day overall performance with dice coefficients of 0.837 on the STARE dataset and 0.832 on the dataset, surpassing present techniques. This development highlights the capability of GANs in scientific imaging, deep gaining knowledge of, and style transfer applications.

关键词： Technological innovation Image segmentation Reviews Annotations Medical services Retina Generative adversarial networks

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A Detailed Analysis of Speculative Based Algorithm Implementation for Modish House

A Detailed Analysis of Speculative Based Algorithm Implement...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Sanjeev Kumar Shah N Siva Kumar E. Gurumoorthi Ammar Hameed Shnain Ashok Kumar Nandhini T J Department of Electronics & Communication engineering Uttaranchal Institute of Technology Uttaranchal University Dehradun India Department of Computer Science and Engineering Aditya Engineering College Surampalem India Department of CSE CMR College of Engineering & Technology The Islamic university Najaf Iraq Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India Department of Computer Science and Engineering Saveetha School of Engineering (SIMATS) Thandalam Chennai Tamil Nadu India

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

In the abruptly evolving vicinity of clever homes, the integration of quick-variety devices through the Internet of Things (IoT) affords an exceptional opportunity and a massive task. The variety of tool communique protocols and the absence of popular production requirements have historically impeded seamless integration, necessitating a sturdy answer. This paper introduces the smart home framework, a progressive IoT-based designed to bridge the gap amongst a massive range of brief-variety devices and man or integration platforms. Leveraging the OSGi ultra-modern, smart allows key protocols along with Zigbee, EnOcean, KNX, X10, Z-Wave for tool verbal exchange, and CoAP (Constrained Application Protocol), MQTT (Message Queuing Telemetry Transport) and XAMPP ((CrossPlatform, Apache, MySQL, PHP, Perl) for application interaction. This framework stands out for its adaptability, extensibility, and the potential to significantly streamline and enhance smart domestic ecosystems.

关键词： Protocols Ecosystems Zigbee Smart homes Production Extensibility Internet of Things

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Efficient Implementation of GF-CNN for Advanced Traffic Sign Recognition in Transportation Systems

Efficient Implementation of GF-CNN for Advanced Traffic Sign...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： A. S. Valarmathy Ashu Katyal Narmadha T L. A. Bewoor P. Karthigaikumar Ayaan Faiz Department of EEE Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Engineering and Technology Department of Computer Science Engineering JAIN (Deemed to be University) Bangalore Karnataka India Computer Vishwakarma Institute of Technology Pune India Department of Electronics and Communication Engineering Karpagam College of Engineering Coimbatore India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Traffic violations cause accidents nowadays. Traffic accidents sometimes result from disobedience of traffic signs owing to low sight, inadequate light, and lack of expertise. ADAS and ITS are the only solutions to prevent accidents. ADAS detects traffic signs and ITS recognizes images for various transport applications. ADAS and ITS can detect minor traffic signs and provide driving information to guarantee road user safety. A key function of ADAS is to inform drivers and traffic signs. One research used traffic sign detection to aid vehicle navigation and driving assistance. Overfitting in the current technique reduces traffic sign detection performance. For image analysis, this study employs Gabor filter, Grey Level Co-occurrence Matrix, and Local Optimal Oriented Pattern feature extraction approaches. Our multi-threshold Otsu approach and cuckoo search method aid in segmentation. The SVM classifier performs traffic sign detection. The hybrid feature extraction approach enhances traffic sign identification by exploring the form, color, and texture of the board. research used an enhanced CNN to recognize traffic signs. Current hybrid feature extraction approach achieves 98.97% accuracy in classifying six traffic signs, however processing time is high for multi-operations. To categorize 10 traffic signs with 99.1% accuracy, an enhanced CNN is presented.

关键词： Support vector machines Accuracy Search methods Feature extraction Autonomous automobiles Convolutional neural networks Gabor filters Time factors Vehicles Accidents

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Joint Visibility Region Detection and Channel Estimation for Extra-Large MIMO Systems

Joint Visibility Region Detection and Channel Estimation for...

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International Symposium on Wireless communication Systems (ISWCS)

作者： Anzheng Tang Jun-Bo Wang Yijin Pan Cheng Zeng Wence Zhang Xiaodan Zhang Yijian Chen Hongkang Yu National Mobile Communications Research Lab Southeast University Nanjing China School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China School of Management Shenzhen Institute of Information Technology Shenzhen China Wireless Product Research and Development Institute ZTE Corporation Shenzhen China

ISBN: (数字)9798350362510

ISBN: (纸本)9798350362527

In this work, we investigate the joint visibility region (VR) detection and channel estimation (CE) problem for extremely large-scale multiple-input-multiple-output (XL-MIMO) systems considering both the spherical wavefront effect and spatial non-stationary (SnS) property. Specifically, we introduce a two-stage VR detection and CE scheme. In the first stage, the belief regarding the visibility of antennas is obtained through a VR detection-oriented message passing (VRDO-MP) scheme, which fully exploits the spatial correlation among adjacent antennas. In the second stage, leveraging the VR information and wavenumber-domain sparsity, we accurately estimate the SnS channel employing the belief-based orthogonal matching pursuit (BB-OMP) method. Simulations show that the proposed algorithms lead to a significant enhancement in VR detection and CE accuracy as compared to existing techniques, especially in low signal-to-noise ratio scenarios.

关键词： Wireless communication Correlation Simulation Precoding Message passing Channel estimation Matching pursuit algorithms

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The Unique Way of Designing SNN Network for Content Recommending System using Hybrid Algorithms

The Unique Way of Designing SNN Network for Content Recommen...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Kanchan Yadav Pinamala Sruthi Hassan M. Al-Jawahry Devender Kumar Anita Gehlot Sanjeev Kumar Angadi Department of Electrical Engineering GLA University Mathura Department of CSE CMR College of Engineering & Technology The Islamic university Najaf Iraq Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India Department of Electronics & Communication engineering Uttaranchal Institute of Technology Uttaranchal University Dehradun India Computer Science and Engineering Nutan College of Engineering and Research Talegaon Dabhade Pune Maharashtra India

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

In the rapidly evolving panorama of e-commerce, personalised apparel recommendation systems stand as a pinnacle of technological advancement, aiming to reflect the intricacies of human style feel and social interactions on-line. This paper offers a singular technique via using Siamese Neural Networks (SNN) to research and integrate style fashion consistency with user social circle data, thereby addressing the pervasive challenges of bloodless start and records sparsity in traditional collaborative filtering systems. Our methodology leverages huge statistics from a distinguished social fashion platform, demonstrating good sized upgrades in advice personalization and accuracy. This integration no longer handiest enhances the user experience by way of providing tailor-made fashion suggestions however additionally marks a great step forward within the application of neural networks in the realm of fashion e-commerce.

关键词： Accuracy Collaborative filtering Neural networks Clothing User experience Electronic commerce Recommender systems

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Image Processing Based Early Breast Cancer Detection in Mammography Images Using GRU and XGBoost Approach

Image Processing Based Early Breast Cancer Detection in Mamm...

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Electronics, Computing, communication and Control Technology (ICECCC), International Conference on

作者： Abhishek Pandey V. Ramesh Ravi Mohan S. Kaliappan Swetha Reddy A K. Gurunathan Department of Computer Applications SCSVMV University Kanchipuram India Department of ECE Shri Ram Institute of Technology Jabalpur Madhya Pradesh India Division of Research and Development Lovely Professional University Punjab India Department of Electronics and Communication Engineering R.M.K. College of Engineering and Technology Chennai India Department of Computer Science and Engineering Mount Zion College of Engineering and Technology Pudukkottai India

ISBN: (数字)9798350371802

ISBN: (纸本)9798350371819

One out of every eight women will be affected by breast cancer at some point in their lives, making it one of the most common malignancies in females. The most reliable method for finding breast cancer in its early stages is mammography. Due to the low visibility and weak contrast of mammographic photos, early identification of breast cancer is crucial for effective therapy. As a result of several computer-aided detection approaches, radiologists are now able to make more precise diagnoses. Operating Sequencing is crucial for several tasks, including morphology, preprocessing, segmentation, feature selection, and training models. The suggested method makes use of preprocessing technologies that can enhance mammography, remove noise, and identify pectoral muscles. Using image processing techniques, a digital image can be “segmented” into many layers. Acquiring a precise image of the mass is the goal of the morphological process. Geometric algorithms are employed by the feature selection technique. Training a GRU-XGBoost model requires careful consideration of which features to use. The proposed method outperforms the current gold standard algorithms, GRU and XGBoost, in every respect. The results showed a remarkable improvement, with an accuracy of 95.63%.

关键词： Training Image segmentation Sequential analysis Accuracy Computational modeling Noise Muscles

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Smart AmbuBag: A Cloud-Based Automated Respiratory Ventilator 13

Smart AmbuBag: A Cloud-Based Automated Respiratory Ventilato...

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13th International Conference on Information and communication Technology Convergence, ICTC 2022

作者： Sampedro, Gabriel Avelino Cobar, Jose Mari Abaja, Keith Daniel Baluis, Ronnie Huyo-A, Shekinah Lor Abisado, Mideth University of the Philippines - Open University Faculty of Information and Communication Studies Los Baños Philippines Research and Development Center Philippine Coding Camp Manila Philippines Technological Institute of the Philippines Computer Engineering Department Quezon City Philippines College of Computing and Information Technologies National University Manila Philippines

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

With the Bag-Valve-Mask (commonly known as an AmbuBag or BVM) being widely used to provide emergency ventilation to patients, it is one of the most critical instruments used in the medical industry. As was emphasized in some studies, there is an urgency to implement a resolution to the human error-induced risks posed by the manual BVM. Hence, this study explores how an IoT-Based Automated Valve Mask can aid in resolving accidents born from the use of the manual counterpart. This device aims to propose a solution to the potential root causes of such adverse events in the field. It uses an GSM-based technology to gather the patient's vital signs and the quality of the air. After being tested on both Child and Adult groups, it was remarked that the accuracy rate of the device surpassed 95%. © 2022 IEEE.

关键词： Internet of things

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Orchestration of Containerized Applications in the Cloud Continuum

Orchestration of Containerized Applications in the Cloud Con...

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IEEE Annual Conference on Pervasive Computing and communications Workshops (PerCom)

作者： Giuseppe Di Modica Antonino Galletta Lorenzo Carnevale Ahmad Alkhansa Alessandro Costantini Daniele Cesini Paolo Bellavista Massimo Villari Department of Computer Science Engineering University of Bologna Italy MIFT Department University of Messina Italy Center for Research and Development on Information and Communication Technologies (CNAF) Italian Institute for Nuclear Physics (INFN) Bologna Italy

Cloud Service orchestration is a hot topic addressed by both the academia and the business world. The increasing diversification of the Cloud offer, coupled with the strong need of flexible and scalable applications demanded by customers, is calling up for efficient and agile mechanisms that streamline the devops practices. The novel opportunity of running software applications or just application portions on resources located on the Edge of the network poses an additional challenge to the already tough problem of flexibly and seamlessly provisioning services. Orchestrators must deal with a continuum of heterogeneous computing resources, contributed by both the Edge and the Cloud, that promises several benefits yet at a higher management cost. In this paper, we introduce a continuum-enabled orchestrator and discuss an application provisioning paradigm that helps the users to easily configure and deploy composite applications. Finally, we showcase the potential of the orchestrator in a simple yet practical smart-city scenario.

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Infinite coexisting attractors in an autonomous hyperchaotic megastable oscillator and linear quadratic regulator-based control and synchronization

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The European Physical Journal B 2023年第1期96卷 1-13页

作者： Alexander, Prasina Emiroğlu, Selçuk Kanagaraj, Sathiyadevi Akgul, Akif Rajagopal, Karthikeyan Department of Electronics and Communication Engineering Chennai Institute of Technology Chennai India Department of Electrical and Electronics Engineering Faculty of Engineering Sakarya University Sakarya Turkey Centre for Nonlinear Systems Chennai Institute of Technology Chennai India Department of Computer Engineering Faculty of Engineering Hitit University Çorum Turkey Department of Electronics and Communications Engineering and University Centre for Research and Development Chandigarh University Mohali India

The coexistence of different attractors with fixed parameters affords versatility in the performance of the dynamical system. As a result, recent research has focused on defining nonlinear oscillators with infinite coexisting attractors, the vast majority of which are non-autonomous systems. In this study, we present an infinite number of equilibriums achieved with the simplest autonomous megastable oscillator. To begin, we explore the symmetry property of distinct coexisting attractors, such as periodic, quasi-periodic, and chaotic attractors. The dynamical properties of the proposed system are further investigated using phase portraits, Lyapunov exponents spectrum, and bifurcation diagram using the local maxima of the state variables. The detailed investigation reveals that the proposed system has a wide range of dynamical properties ranging from periodic oscillations to hyperchaos. Linear quadratic regulator (LQR) control-based controllers are designed to control the chaos in the proposed system and also achieve synchronization between proposed systems that have different initial conditions. The effectiveness of the controller designed based on LQR is presented by simulation results. It is shown that the proposed system converges asymptotically to the desired equilibrium point, and the synchronization between the slave and the master systems is achieved by converging the error to zero after controllers are activated.

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Air Quality Improvement and Optimisation Using Machine Learning Technique 2

Air Quality Improvement and Optimisation Using Machine Learn...

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2nd IEEE International Conference on Advances in Computing, communication and Applied Informatics, ACCAI 2023

作者： Veeranjaneyulu, Rayavarapu Boopathi, Sampath Kumari, R. Krishna Vidyarthi, Anurag Isaac, J. Samson Jaiganesh, V. Pace Institute of Technology & Sciences Department of Computer Science Engineering Andhra Pradesh Ongole523272 India Muthayammal Engineering College Department of Mechanical Engineering Tamil Nadu Namakkal637408 India College of Engineering and Technology Srm Institute of Science and Technology Department of Career Development Centre Tamil Nadu Kattankulathur603203 India Graphic Era Deemed to Be University Department of Electronics & Communication Engineering Uttarakhand Dehradun248002 India Karunya Institute of Technology and Sciences Department of Biomedical Engineering Tamil Nadu Coimbatore641114 India Bharath Institute of Higher Education and Research Department of Mechanical Engineering Tamil Nadu Chennai600063 India

ISBN: (纸本)9798350315905

Due to the increased use of automobiles, the manufacturing industry, and the emission of pollutants from other human activities, air pollution has risen above the expected safety level. Accurate estimating of the air quality index(AQI) is essential for effective pollution control. In this research, an AQI prediction ANFIS network model was created utilizing an already-existing data set. In this instance, the ANFIS system compares the performances of the back propagation neural network model, hybrid models, the Gaussian-BNN model, and the Gaussian-hybrid BNN model. Based on the actual raw data set, it was noted that the R and IA values of the Gaussian hybrid model are 0.9899. The ANFIS gauss-hybrid model might therefore be used to predict the most accurate model data. © 2023 IEEE.

关键词： Quality control

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