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Hajj Crowd Management Using CNN-Based Approach

computers, Materials & Continua 2021年第2期66卷 2183-2197页

作者： Waleed Albattah Muhammad Haris Kaka Khel Shabana Habib Muhammad Islam Sheroz Khan Kushsairy Abdul Kadir Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Electronic Section Universiti Kuala Lumpur British Malaysian InstituteMalaysia Department of Electrical Engineering Onaizah CollegesSaudi Arabia Department of Electrical and Computer Engineering International Islamic UniversityMalaysia

Hajj as the Muslim holy pilgrimage,attracts millions of humans to Mecca every *** to statists,the pilgrimage has attracted close to 2.5 million pilgrims in 2019,and at its peak,it has attracted over 3 million pilgrims in *** is considered as the world’s largest human *** makes one of the main concerns with regards to managing the large crowds and ensuring that stampedes and other similar overcrowding accidents are *** paper presents a crowd management system using image classification and an alarm system for managing the millions of crowds during *** image classification system greatly relies on the appropriate dataset used to train the Convolutional neural network(CNN),which is the deep learning technique that has recently attracted the interest of the research community and industry in varying applications of image classification and speech *** core building block of CNN is is a convolutional layer obtained by the getting CNN trained with patches bearing designated features of the trainee *** algorithm is implemented,using the Conv2D layers to activate the CNN as a sequential ***,creating a 2D convolution layer having 64 filters and drop out of 0.5 makes the core of a CNN referred to as a set of *** aim is to train the CNN model with mapped image data,and to make it available for use in classifying the crowd as heavily-crowded,crowded,semi-crowded,light crowded,and *** utility of these results lies in producing appropriate signals for proving helpful in monitoring the *** pilgrims from the photos will help the authorities to determine the number of people in certain *** results demonstrate the utility of agent-based modeling for Hajj pilgrims.

关键词： Crowd management CNN approach Hajj

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Automated Driving Licensing System Based on Wireless Sensor Networks and IoT

Automated Driving Licensing System Based on Wireless Sensor ...

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Intelligent Computing and Control for engineering and Business Systems (ICCEBS)

作者： Prasanna R Jenath M Banu Priya Prathaban M.H. Masood H Umma Habiba Rajesh R Kanna Department of Electronics and Communication Engineering SRM Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Sri Sai Ram Engineering College Chennai India Department of Electronics and Communication Engineering Bharath Institute of Higher Education and Research Chennai India Department of Computer and Science Engineering SRM Institute of Science and Technology Chennai India

The advent of autonomous vehicles has revolutionized the automotive industry, offering promising advancements in safety, efficiency, and mobility. To integrate these autonomous vehicles into our society seamlessly, it is imperative to establish a robust licensing system that ensures the safe operation of automated vehicles on public roads. This paper presents an effective Automated Driving Licensing System (ADLS), a digital framework designed to regulate and license autonomous vehicles. The ADLS leverages cutting-edge technology, including artificial intelligence, machine learning, and secure data management, to evaluate and validate the capabilities of autonomous vehicle systems. Through a comprehensive assessment process, the system assesses the vehicle's ability to navigate various road conditions, respond to dynamic environments, and prioritize safety for passengers and other road users. The automated licensing issuance system ensures that only vehicles meeting predefined safety and performance criteria are granted licenses. Successful licensing is a testament to the vehicle's readiness for autonomous operation within specified geographic regions and under predefined conditions. The proposed system offers a comprehensive approach to safely integrate autonomous vehicles into our transportation ecosystem. Furthermore, the ADLS establishes a framework for continuous monitoring and updates, enabling ongoing assessments to keep pace with evolving technology and safety standards.

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UMPS: A Unified Multi-Physics Simulation Framework for Photonic Device Thermal Management with Tri-Domain Integration

UMPS: A Unified Multi-Physics Simulation Framework for Photo...

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International Conference on Information Science and communications Technologies (ICISCT)

作者： Kanchana Chathurangi Ahangama Chinthaka Premachandra Department of Electrical and Computer Engineering The Open University of Sri Lanka Nawala Sri Lanka Department of Advanced Electronic Engineering School of Engineering and Graduate School of Engineering and Science Shibaura Institute of Technology Tokyo Japan

ISBN: (数字)9798331527921

ISBN: (纸本)9798331527938

The wavelength instability of Distributed Feedback (DFB) lasers in telecommunication degrades system performance. While DFB lasers are popular for their stability in singlewavelength output generation, thermal fluctuation severely impacts them. Excessive heat also causes complete device failure. However, effective thermal management is a critical challenge in the design and operation of DFB lasers. The interaction among multiple properties of electrical, optical, and thermal domains makes it more challenging. To address this challenge, this study introduces a Unified MultiPhysics Simulation ( UMPS ) model that integrates electrical, optical, and thermal dynamics into a single, comprehensive framework. This innovative approach demonstrates the capability of accurate predictions of temperature distributions, identification of thermal hotspots, and optimization of thermal management strategies. The simulation conducted by the UMPS model captures both steady-state and transient thermal behaviors, providing valuable insights into how photonic devices respond to dynamic operating conditions. The proposed UMPS model not only advances the understanding of thermal dynamics in DFB lasers but also offers a versatile tool that can be applied to other photonic devices facing similar challenges. This model can potentially impact the design of next-generation photonic systems, ensuring their efficiency and reliability in increasingly demanding applications.

关键词： Integrated optics Laser theory Temperature distribution Optical feedback Distributed feedback devices Laser feedback Thermal management Laser modes Thermal stability Photonics

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300-Gbit/s/λ 8-Level Pulse-Amplitude-Modulation (PAM8) with a silicon microring modulator utilizing long short term memory regression and deep neural network classification

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Optics and Laser Technology 2024年 171卷

作者： Hung, Tun-Yao Chan, David W.U. Peng, Ching-Wei Chow, Chi-Wai Tsang, Hon Ki Department of Photonics & Graduate Institute of Electro-Optical Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Department of Photonics & Graduate Institute of Electro-Optical Engineering College of Electrical and Computer Engineering National Chiao Tung University Hsinchu30010 Taiwan Department of Electronic Engineering The Chinese University of Hong Kong Shatin Hong Kong

We present the first experimental demonstration up the authors' knowledge a high-capacity short-reach optical communication link which employs a long-short-term-memory (LSTM) and deep-neural-network (DNN) for enabling 300-Gbit/s (100 Gbaud) 8-level pulse amplitude modulation (PAM8) generated by a single 55-GHz bandwidth silicon microring modulator (SiMRM) with a driving voltage of 1.8-Vpp. As PAM8 modulation has a lower average level-to-level transition energy when compared with that in PAM4, it could be possible to achieve higher energy efficiency when utilizing PAM8 at the same driving voltage. To recover the PAM8 modulation, LSTM and DNN are utilized for regression and classification respectively. The LSTM contains memory cells for dealing with signal time domain dependencies, and has the ability to store, read, and reject data passing through the neural network. The subsequent DNN performs the classification of the 8 levels. Experimental results show that 300 Gbit/s PAM8 modulation is achieved at back-to-back (B2B) and 270 Gbit/s PAM8 is achieved after 1 km standard single-mode-fiber (SSMF) transmission satisfying the soft-decision forward error correction (SD-FEC) bit-error-rate requirement (BER) of 2.4 × 10-2. © 2023 Elsevier Ltd

关键词： Deep neural networks

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A Deep Convolutional Kernel Neural Network based Approach for Stock Market Prediction using Social Media Data 7

A Deep Convolutional Kernel Neural Network based Approach fo...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Agarwal, Varsha Ravi Kumar, P. Shankar, Shiva Praveena, S. Dubey, Vivek Chauhan, Amit ATLAS SkillTech University ISME Maharashtra Mumbai India K.Narayanapura Department of Media Studies Karnataka Bangalore India Computational Intelligence Srmist Kattankulathur Tamil Nadu Chengalpattu India Department of Electronics and Communication Engineering Telangana Hyderabad India Computer Science and Engineering Bharat Institute of Engineering and Technology Telangana Hyderabad India Department of Life Sciences Karnataka Bengaluru India

ISBN: (纸本)9798350397253

Several economists and social scientists have held a longstanding fascination with the practice of stock market prediction. As the stock market is essentially uncontrollable chaos, many experts believe that trying to predict it is futile. Due to the complexity of the numerous factors, accurate stock price predictions are notoriously difficult to achieve. While the market behaves more like a scale than a voting machine over the long run, its behavior may be predicted with some certainty. Information from Twitter is used into the algorithm. In this proposed method, a convolutional extreme learning machine model with kernel support was introduced (CKELM). To improve feature extraction and data classification, the CKELM model builds on the KELM's hidden layer by adding convolutional and subsampling layers. The convolutional layer and the subsampling layer do not employ the gradient technique to fine-tune their parameters because some designs worked well with random weights. When compared to popular models like CNN and KELM, The proposed model fares quite well, with an accuracy of around 98.3 percent. © 2023 IEEE.

关键词： Convolutional neural networks

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Intelligent Breast Abnormality Framework for Detection and Evaluation of Breast Abnormal Parameters

Intelligent Breast Abnormality Framework for Detection and E...

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2022 International Conference on Edge Computing and Applications, ICECAA 2022

作者： Ajay, P. Kawale, Sheetalrani R. Sharma, Avinash Diwan, Supriya P. Dankan Gowda, V. Department of Electronics and Communication Engineering Anna University Tamilnadu Chennai India Department of Computer Science Karnataka State Akkamahadevi Women's University Karnataka Vijayapura India Mullana Haryan Ambala India Department of Electronics and Telecommunication Engineering Government College of Engineering Satara Maharashtra Karad India Department of Electronics and Communication Engineering BMS Institute of Technology and Management Karnataka Bangalore India

ISBN: (数字)9781665482325

ISBN: (纸本)9781665482325

Unlike the healthy cells in the breast tissue, cancerous breast cells are unwelcome and have strange properties. In both sexes, this will quickly expand and infiltrate adjacent tissue, leading to the formation of a tumour. Using the Intelligent-Breast Abnormality Detection (I-BAD) framework, many breast cancer parameters are evaluated in this article. It has already been shown that some indicators may be used for early detection of breast cancer. There is also discussion of the instruments and strategies that facilitate the monitoring of the selected breast health metrics. Classification methods that use machine learning to store and analyse data are also discussed. The suggested I-BAD framework's process is then visually shown in clean drawings. © 2022 IEEE.

关键词： Internet of things

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Nano-scale Quantum Modified SnTe/Pb0.98Sn0.02Te Supper lattice Structure Based Highly Sensitive Exotic p+-i-n+ IR Photo-detector

Nano-scale Quantum Modified SnTe/Pb0.98Sn0.02Te Supper latti...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Bias Bhadra Suchismita Chinara Abhijit Kundu Department of Computer Science Engineering National Institute of Technology Rourkela Odissa India Department of Electronics & Communication Engineering Chaibasa Engineering College Jharkhand India

In this paper, asymmetrical super-lattice SnTe/Pb 0.98 Sn 0.02 Te exotic p + -i-n + nano-scale photo-sensor has been reported with significant improvement of photo-responsivity and quantum efficiency in the near infrared wavelength region. The photo-electric characteristics of the proposed Device-Under-Test (DUT) are studied by developing self consistent Non-linear Quantum Modified Drift-Diffusion (NQMDD) Model with appropriate boundary conditions. The proposed NQMDD model is validated by comparing the simulated results with the experimental results under a-like operating-conditions. An excellent agreement, in between the simulated and experimental results, under a-like operating-conditions, reveals the validity of the proposed NQMDD simulator. The performance of the DUT is enhanced by incorporation of alternate thick – SnTe and thin- Pb 0.98 Sn 0.02 Te layers into the active region of the device. The extensive study proves that the photo-electric characteristics of the DUT have improved significantly compared to its Si counterpart. To the best of authors’ knowledge this is the first report on asymmetrical super-lattice SnTe/Pb 0.98 Sn 0.02 Te exotic p + -i-n + nano-scale IR photo-sensor. The proposed DUT will play an important role for device-engineering for the development of highly sensitive circuits in defence and bio-medical industries.

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Implementing LoRa MIMO System for Internet of Things

arXiv

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arXiv 2025年

作者： Ghosh, Atonu Chandan, Sharath Misra, Sudip Department of Computer Science and Engineering Indian Institute of Technology Kharagpur Kharagpur721302 India Department of Electronics and Communication Engineering Sona College of Technology Tamil Nadu Salem636005 India

Bandwidth constraints limit LoRa implementations. Contemporary IoT applications require higher throughput than that provided by LoRa. This work introduces a LoRa Multiple Input Multiple Output (MIMO) system and a spatial multiplexing algorithm to address LoRa’s bandwidth limitation. The transceivers in the proposed approach modulate the signals on distinct frequencies of the same LoRa band. A Frequency Division Multiplexing (FDM) method is used at the transmitters to provide a wider MIMO channel. Unlike conventional Orthogonal Frequency Division Multiplexing (OFDM) techniques, this work exploits the orthogonality of the LoRa signals facilitated by its proprietary Chirp Spread Spectrum (CSS) modulation to perform an OFDM in the proposed LoRa MIMO system. By varying the Spreading Factor (SF) and bandwidth of LoRa signals, orthogonal signals can transmit on the same frequency irrespective of the FDM. Even though the channel correlation is minimal for different spreading factors and bandwidths, different Carrier Frequencies (CF) ensure the signals do not overlap and provide additional degrees of freedom. This work assesses the proposed model’s performance and conducts an extensive analysis to provide an overview of resources consumed by the proposed system. Finally, this work provides the detailed results of a thorough evaluation of the model on test hardware. © 2025, CC BY-NC-ND.

关键词： Orthogonal frequency division multiplexing

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Improving Tomato Disease Diagnosis Through ResNet50: An Innovative Approach

Improving Tomato Disease Diagnosis Through ResNet50: An Inno...

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Sustainable Expert Systems (ICSES), International Conference on

作者： R. Dhamotharan J. Relin Francis Raj R. Santhana Krishnan S. Kavitha N Soundiraraj V. Vinoth Kumar Department of Computer Science and Information Technology Kalasalingam Academy of Research and Education Krishnankoil India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India Department of Electrical and Electronics Engineering PSNA College of Engineering and Technology Dindigul India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Tomato crops are vulnerable to numerous diseases that can severely affect both yield and quality. Early and precise identification of these diseases is indispensable for implementing active management and intervention measures. This research introduces an advanced approach to tomato disease detection by employing the ResNet50 architecture, pretrained on the ImageNet dataset. Through transfer learning, the ResNet50 model leverages its pre-learned features to classify tomato plant images affected by diseases such as Tomato Mosaic Virus (ToMV), Bacterial Spot, Blossom End Rot, Early Blight, and Late Blight. The ResNet50 model is fine-tuned with custom classification layers tailored to the specific diseases, allowing it to achieve high accuracy in disease detection. Preliminary results indicate that the ResNet50 model significantly outperforms traditional machine learning approaches, demonstrating its efficacy in accurately diagnosing tomato diseases. This study provides an effective solution for improving disease detection in tomatoes and highlights the transformative role of deep learning in advancing agricultural practices, fostering sustainability, and enhancing productivity.

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Design of a 7.5 MVA Automated Substation with Fault Analysis Using ETAP Software 5

Design of a 7.5 MVA Automated Substation with Fault Analysis...

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5th International Conference on Electrical engineering and Information and communication Technology, ICEEICT 2021

作者： Ahona, Munazza Batul Sneha, Fariha Hakim Rahman, Atik Yasir Ankhi, Atia Islam Khan, Md. Ali Azam Military Institute of Science and Technology Department of Electrical Electronic and Communication Engineering Dhaka Bangladesh

ISBN: (纸本)9781665495226

Substations are undeniably risk prone environments with a distinctive set of hazards often concealed from the eyes of general people. One footstep in the erroneous direction can lead people too near to functioning equipment, and even an accidental movement of an arm touching a control switch can put the entire area to a grinding halt. So, the study of safety factor is needed in establishing a substation. ETAP is the most extensive analysis tool for the design, automation, simulation, and operation of generation, distribution, and industrial power systems. The study of this work comprises the designing of 7.5 MVA (33/11kV) substation, selection of components, substation base plant with standard simulation - Load flow analysis, Short circuit calculation, Circuit breaker coordination, Protection device coordination. © 2021 IEEE.

关键词： Electric circuit breakers

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