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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Vivek V Aditya Kumar S. Yuvaraj Vaishali A Ashu Katyal Girija Shankar Sahoo Department of Computer Science and Engineering Faculty of Engineering and Technology Bangalore Karnataka India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Engineering Vishwakarma Institute of Information Technology Pune INDIA Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

This paper affords a progressive approach to high-pace wide area fiber optic (WAFO) communications by way of combining radio frequency (RF) and optical spectral optimization strategies. This method presents a new approach to the longstanding task of reaching high-pace, long-distance transmission over current networks. The optimization consists of maximizing the spectral performance of the RF and optical structures with the aid of the use of techniques which includes frequency-agile modulation codec's, linear dispersion compensation, and spectral shaping strategies. Moreover, dynamic optical and RF network routing algorithms are proposed to enhance potential and reduce latency. The proposed optimization is confirmed thru enormous simulation and numerical analysis. Results indicate big overall performance gains in phrases of spectral performance, records price, postpone, and reliability. This joint RF-optical optimization approach is an efficient and value-effective answer for WAFO networks and ought to notably benefit large-scale and future WAFO deployments.

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Rlsnake: A hybrid reinforcement learning approach for road detection 24

Rlsnake: A hybrid reinforcement learning approach for road d...

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2021 24th ISPRS Congress Commission III: Imaging Today, Foreseeing Tomorrow

作者： Botteghi, N. Sirmacek, B. Ünsalan, C. Robotics and Mechatronics Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Netherlands Smart Cities School of Creative Technology Saxion University of Applied Sciences Netherlands Electrical and Electronics Engineering Faculty of Engineering Marmara University Turkey

Road network detection from very high resolution satellite and aerial images is highly important for diverse domains. Although an expert can label road pixels in a given image, this operation is prone to error and quite time consuming remembering that road maps must be updated regularly. Therefore, various computer vision based automated algorithms have been proposed in the last two decades. Nevertheless, due to the diversity of scenes, the field is still open for robust methods which might detect roads on different resolution images of different type of environments. In this study, we picked an earlier proposed road detection method which works based on traditional computer vision and probability theory algorithms. We improved it by further steps using reinforcement learning theory. With the help of the novel hybrid technique (traditional computer vision method combined with reinforcement learning based artificial intelligence), we achieved a solution that we call RLSnake. This new method can learn new image scenes and resolutions rapidly and can work reliably. We believe that the proposed RLSnake will be a significant step in the remote sensing field in order to develop solutions which might increase performance by combining the power of traditional and new techniques. © 2021 International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives. All rights reserved.

关键词： Reinforcement learning

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Text Mining for Intelligent Information Processing and Analysis

Text Mining for Intelligent Information Processing and Analy...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Adarsh Srivastav G. Indira Shweta A. Tiwaskar Avni Garg Shweta Singh Sunanda Das Department of Computer Science & Engineering Vivekananda Global University Jaipur India Department of Electrical and Electronics Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Engineering Vishwakarma Institute of Information Technology Pune INDIA Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Engineering Faculty of Engineering and Technology Bangalore Karnataka India

textual content Mining is the process of extracting meaningful information from large volumes of unstructured text. it's far a form of synthetic intelligence used to investigate and interpret files along with consumer feedback, emails, transcripts, net logs, surveys, and on line documents. Its intention is to discover hidden shape, patterns, institutions, and correlations in textual facts in order to draw inferences and broaden new insights. textual content Mining allows a deeper level of intelligence and selection-making by means of uncovering which means from unstructured textual facts. by means of utilising present day statistical strategies and herbal Language Processing (NLP), text Mining can produce automatic fashions that interpret and translate textual content into intelligent information which can be used to make correct predictions and provide actionable insights. textual content Mining is also being used to find out new trends, categorize client sentiment, locate correlations in purchaser comments, and section document collections. eventually, text Mining is being used to assist automate selection-making by automating workflows, processing massive volumes of text in a fragment of time, and providing deep insights into patron conversations.

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Developing a computer-Aided Diagnostic System for Breast Cancer Ultrasound Imaging

Developing a Computer-Aided Diagnostic System for Breast Can...

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Data Science and engineering in Healthcare, Medicine and Biology, IEEE EMBS Special Topic Conference on

作者： Radwa Taha Shereen Afifi Mohamed A. Abd-ElGhany Mohammed A.-M Salem Master Candidate in the Faculty of Media Engineering and Technology German University in Cairo New Cairo Egypt Assistant Professor in Computer Science Engineering Faculty of Media Engineering and Technology German University in Cairo New Cairo Egypt Professor in Electrical Engineering Faculty of Information Engineering and Technology German University in Cairo New Cairo Egypt Integrated Electronics Systems Lab TU Darmstadt Germany Digital Media Engineering Faculty of Media Engineering and Technology German University in Cairo New Cairo Egypt

In this research we aim to develop a computer-aided diagnostic system (CAD) for breast ultrasound imaging to enhance the early detection of breast cancer. CAD tools aid physicians in the diagnostic process leading to early detection and treatment of breast cancer. The proposed CAD tool comprises three stages, the first is enhancement of breast ultrasound imaging by applying custom segmentation deep learning architectures [U-Net, SK-U-Net, RDA-U-Net] to enhance the isolation of masses in Ultrasound images, then generation of new ultrasound images by employing Generative Adversarial Networks [DAGAN, DCGAN] to overcome data limitation in available data-sets, and the last stage is the detection and classification of breast cancer masses by a CNN model. The enhancement stage shows promising results with accuracy of 98 % overall accuracy, 92 % overall precision, and 85 % overall *** Relevance: Breast cancer is one of the deadly diseases in the world. The earlier the detection the higher the chances of recovery. Thus a CAD tool that facilitates the screening process of ultrasound images will aid physicians in the early detection and diagnosis of the disease.

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Retraction Note: Machine learning aided design and optimization of MEMS optical phased array with silicon micro mirrors for nanofabrication

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Optical and Quantum electronics 2024年第10期56卷 1-2页

作者： Premalatha, S. Sharma, Himanshu Vekariya, Vipul Shrivastava, Abhinav Ramkumar, G. Metwally, Ahmed Sayed M. Aftab, Sikandar Department of Electronics and Communication Engineering K S R Institute for Engineering and Technology Tiruchengode India Department of Computer Engineering and Applications GLA University Mathura India Department of Computer Science and Engineering Parul Institute of Engineering and Technology Parul University Waghodia India Faculty of Law Symbiosis Law School (SLS) Symbiosis International (Deemed University) (SIU) Pune India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Mathematics College of Science King Saud University Riyadh Saudi Arabia Department of Intelligent Mechatronics Engineering Sejong University Seoul South Korea

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Review of Energy Management Systems for Small and Medium Enterprises

Review of Energy Management Systems for Small and Medium Ent...

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Junior Conference on Lighting (Lighting)

作者： Gergana Vacheva Plamen Stanchev Nikolay Hinov Departement of Power Electronics Faculty of Electronic Engineering and Technologies Technical University of Sofia Sofia Bulgaria Departement of Information Technology in Industry Faculty of Computer Systems and Technologies Technical University of Sofia Sofia Bulgaria

This article provides an overview of systems and methodologies used for energy management in small and medium-sized industrial enterprises. The main goal of the presented development is to examine basic principles in the management of energy flows in industrial enterprises powered by various renewable energy sources and energy storage elements. With the addition of additional energy sources, energy efficiency would significantly increase, which would also lead to a reduction in electricity costs.

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A Comparative Simulation Study of DG-MOSFETs: PCMS Approach in FETMOSS vs. CMS in Silvaco TCAD 3

A Comparative Simulation Study of DG-MOSFETs: PCMS Approach ...

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3rd Novel Intelligent and Leading Emerging Sciences Conference, NILES 2021

作者： Salem, Marwa S. El-Banna, Mohamed Abouelatta, Mohamed Saeed, Ahmed Shaker, Ahmed University of Ha'il Department of Computer Engineering Computer Science and Engineering College Ha'il Saudi Arabia Ain Shams University Engineering Physics and Mathematics Department Faculty of Engineering Cairo Egypt Ain Shams University Electronics and Communication Department Faculty of Engineering Cairo Egypt Future University in Egypt Electrical Engineering Department Faculty of Engineering and Technology Cairo Egypt

ISBN: (纸本)9781665421577

The simulation of quantum transport in DG-MOSFETs could be effectively accomplished by the Partial-Coupled Mode Space (PCMS) approach, which is realized by separating the odd and even modes solutions. This technique combines the merits of Coupled Mode Space (CMS) regarding the accuracy and Uncoupled Mode Space (UMS) as far as reducing computational time is concerned. In this work, a comparison study between PCMS using our developed FETMOSS simulator and CMS using Silvaco TCAD is carried out. The simulation is performed on a set of short-channel DG-MOSFETs. The accuracy at room temperature is found to be less than 8% along the whole range of the supply voltage. Based on this study, the PCMS approach in FETMOSS simulator is validated and proved to trace device performance in reasonable times compared to the TCAD high computational times. © 2021 IEEE.

关键词： MOSFET devices

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Evolving Restricted Boltzmann Machine-Kohonen Network for Online Clustering

arXiv

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arXiv 2024年

作者： Senthilnath, J. Bhattiprolu, Adithya Singh, Ankur Zhou, Bangjian Wu, Min Benediktsson, Jón Atli Li, Xiaoli Singapore138632 Singapore The Department of Electrical & Electronics Engineering Birla Institute of Technology and Science Pilani333031 India The Faculty of Electrical and Computer Engineering University of Iceland Reykjavik107 Iceland School of Computer Science and Engineering Nanyang Technological University Singapore and A*STAR Centre for Frontier AI Research Singapore138632 Singapore

A novel online clustering algorithm is presented where an Evolving Restricted Boltzmann Machine (ERBM) is embedded with a Kohonen Network called ERBM-KNet. The proposed ERBM-KNet efficiently handles streaming data in a single-pass mode using the ERBM, employing a bias-variance strategy for neuron growing and pruning, as well as online clustering based on a cluster update strategy for cluster prediction and cluster center update using KNet. Initially, ERBM evolves its architecture while processing unlabeled image data, effectively disentangling the data distribution in the latent space. Subsequently, the KNet utilizes the feature extracted from ERBM to predict the number of clusters and updates the cluster centers. By overcoming the common challenges associated with clustering algorithms, such as prior initialization of the number of clusters and subpar clustering accuracy, the proposed ERBM-KNet offers significant improvements. Extensive experimental evaluations on four benchmarks and one industry dataset demonstrate the superiority of ERBM-KNet compared to state-of-the-art approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Clustering algorithms

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Evaluation of Drivers' Eco-Safe Driving Behaviour with Fuzzy Logic Classifier and Machine Learning Approaches

SSRN

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SSRN 2024年

作者： Çeven, Süleyman Albayrak, Ahmet Bayır, Raif Department of Mechatronics Engineering The Institute of Graduate Studies Karabük University Karabük Turkey Department of Electronics and Automation Technology Düzce Vocational School Düzce University Düzce Turkey Department of Computer Engineering Faculty of Technology Düzce University Düzce Turkey Department of Mechatronics Engineering Faculty of Technology Karabük University Karabük Turkey

Road traffic fatalities and injuries remain a major global health and development challenge. According to the World Health Organization (WHO) global status report on road safety (2023), the number of road traffic fatalities has decreased slightly to 1.19 million per year, indicating that efforts to improve road safety are effective. Driver behaviour is known to be the main cause of traffic accidents. Drivers' behaviours such as steering, braking and acceleration are behaviours that have important effects on traffic. In this study, eco-safe driving behaviours of drivers were studied with the data obtained through in-vehicle sensor data of 16 drivers. In the experimental studies, vehicle parameters were obtained via On-board Diagnostics (OBD) interface on a driving route determined in Düzce province of Türkiye. In the literature, eco driving and safe driving have been studied separately. In this study, a new methodology is proposed to consider eco driving and safe driving together. The proposed methodology is developed with data labelling technique based on fuzzy logic approach. Through this process, drivers' eco-safe driving behaviours were classified into five different categories. After the eco-safe driving index was obtained with fuzzy logic, MLP (Multilayer Perceptron), Ensemble Learning and SVM (Support Vector Machine) were used to predict the driving behaviour of the drivers. In the experimental studies, both the accuracy of data labelling with fuzzy logic has been demonstrated and the performance of the proposed approach has been considered acceptable. © 2024, The Authors. All rights reserved.

关键词： Fuzzy logic

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Edge Computing Enabled Smart Warehouse Management System for Food Processing Industries

Edge Computing Enabled Smart Warehouse Management System for...

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International Conference on Computing and Networking technology (ICCNT)

作者： Gowrishankar V Veena P Ponmurugan P Annapoorani B T Vijayakumar P Siva Ramkumar M Department of Electronics and Communication Engineering Velalar College of Engineering and Technology Erode India Department of Electrical and Electronics Engineering K S R Institute for Engineering and Technology Tiruchengode India Department of Research Meenakshi Academy of Higher Education and Research Chennai Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Sathyamangalam India Department of Master of Computer Applications Kongu Engineering College Perundurai Tamil Nadu Department of Electrical and Electronics Engineering Faculty of Engineering Karpagam Academy of Higher Education Coimbatore India

Food safety is a primary concern across all global platforms, and food storage is a crucial and essential part of management. This clarifies that food warehousing should be fully automated or has some management system. Good food storage plays a vital role in food security, which is affected by food waste and food loss. As a result, it is critical to identify and remove microbial contamination before it spreads to other items. To end that, we propose an edge computing-enabled intelligent warehouse system with an ESP32 microcontroller and micropython. The food commodities studied in the other papers were fish, dairy, fresh food products, crop-field cucumber, seafood, wine, and pre-packaged food. The various gas sensors are used to detect microbial growth, food decay, and shelf life. Humidity and temperature sensors are employed to monitor the warehouse environment, along with RFID technology to track food items. Multiple nodes accessible via Telegram and dashboard notifications will be put in the building to enable law enforcement collaboration. These nodes will exchange information on the building's structure. Compared to the traditional warehouse management system, the proposed edge computing-enabled IoT warehouse shelves reduce waste and contamination in fruit and vegetable chambers. Designs are experimented with different sets of fruits and vegetables. Continuous live monitoring and an alert system demonstrate the effectiveness of the proposed innovative framework compared with an existing management system.

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