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Innovative Sustainable Technologies for Energy, Mechatronics, and Smart systems (ISTEMS), International Conference on

作者： Basel A. Dabwan Mukti E. Jadhav Mohammed Al Yami Eman A. Hassan Soad M. Almula Yahya A. Ali Department of Information System Albaydha University Albaydha Yemen Department of Computer Science Shri Shivaji Science & Art College line India Department of information Systems Najran University Kingdom of Saudi Arabia Department of Computer Science Najran University Kingdom of Saudi Arabia

ISBN: (数字)9798350384246

ISBN: (纸本)9798350384253

Approximately 466 million people worldwide experience deafness or hearing impairment. These individuals face the challenge of connecting with others., acquiring new knowledge., and participating in various activities. gesture language acts as a crucial bridge., facilitating communication between them and the broader world. However., automating the recognition of sign language poses numerous challenges. To address this., we utilizing the MobileNetV2 Model. Our approach involved utilizing MobileNetV2 Model with dataset that represents the ASL Alphabet., encompassing 24 classes corresponding to English gesture language letters., (excluding the letters J and Z., which necessitate movement). The training dataset comprises 27.,455 entries. cases., while the test dataset comprises 7.,172 cases. In our training process., 80% of the dataset was designated for training., with the remaining 20% utilized for testing. The results of our proposed model were highly promising., achieving a remarkable accuracy rate of 99.9% and 100% validation accuracy. This success indicates the potential effectiveness of our model in automatically recognizing American Sign Language gestures., thereby contributing to improved communication and accessibility for individuals with hearing challenges.

关键词： Training Bridges Sign language Deafness Accuracy Mechatronics Face recognition

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Comprehensive analysis of energy efficient secure routing protocol over sensor network

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Materials Today: Proceedings 2022年 62卷 5003-5007页

作者： Chandra Saxena, Mohit Banu, Firdouse Shrivastava, Anurag Thyagaraj, M. Upadhyay, Shrikant SCSE Galgotias University Greater Noida India Department of Applied Information System Applied College King Khalid University Mahala Saudi Arabia Sushila Devi Bansal College of Engineering Indore India Department of Computer Applications Nazareth College of Arts and Science Avadi Chennai India Department of ECE Cambridge Institute of Technology Jharkhand Ranchi India

To make hierarchical wireless sensor network transmission more secure, node life longer, and network operation efficiency higher, a WSN shared vital management scheme based on LEACH protocol is proposed. The program has good integrity and timeliness, dynamic critical security, node security independence, and critical survival effectiveness. It also can resist risks such as man-in-the-middle attacks and DDoS attacks, and it is expensive to distribute shared keys. The energy is lower, and the network operation efficiency is higher. © 2022

关键词： Sensor nodes

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Weighted Mask Recurrent-Convolutional Neural Network based Plant Disease Detection using Leaf Images 7

Weighted Mask Recurrent-Convolutional Neural Network based P...

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

作者： Sharmila, R. Kamalitta, R. Moorthy Singh, Devesh Pratap Chauhan, Amit Acharjee, Purnendu Bikash Dhanalakshmi Srinivasan Engineering College Department of MCA Tamilnadu Perambalur India Information Technology K.Ramakrishnan College of Engineering Tamilnadu Trichy India Electronics Engineer Production Dhaksha Unmanned System Pvt Ltd Tamilnadu Chennai India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttrakhand Dehradun India Department of Life Sciences Karnataka Bengaluru India CHRIST University Faculty Member Computer Science Maharashtra Pune India

ISBN: (纸本)9798350397253

Large losses in output, money, and quality/quantity of agricultural goods are incurred due to plant diseases. Seventy percent of India's GDP is tied to the agricultural sector, thus protecting plants from diseases is crucial. For this reason, it is important to keep an eye on plants from the moment they sprout. The usual approach for this omission is naked eye inspection, which is more time-consuming, costly, and requires significant skill. Thus, automating the method for detecting diseases is necessary to speed up this process. It is imperative that image processing methods be used in the creation of the illness detection system. Disease detection involves a number of processes, including Weighted Mask R-CNN, GLCM feature extraction, Multi-thresholding image pre-processing, and K means image segmentation classification. The weighted Mask R-CNN outperforms the standard RNN, the Mask R-CNN, and the CNN in terms of accuracy and recall in analytical trials by a significant margin. © 2023 IEEE.

关键词： Recurrent neural networks

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Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Learning 22

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Lea...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Cao, Weipeng Yao, Xuyang Xu, Zhiwu Pan, Yinghui Sun, Yixuan Li, Dachuan Qiu, Bohua Wei, Muheng Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (纸本)9798331527471

Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model’s capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods. © 2024 IEEE.

关键词： Zero-shot learning

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Parallel Byzantine fault tolerance method for blockchain

Parallel Byzantine fault tolerance method for blockchain

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Dubey, Kumar Pradyot Gnanaprakasam, C.N. Khan, Ihtiram Raza Sadik, Md Shibli Al-Farhani, Liwa H. Ray, Samrat Department of Computer and Information Science Jagadguru Ram Bhadracharya Divyanga University UP Chitrakoot India Department of Electronics and Instrumentation Engineering St. Joseph’s College of Engineering Tamilnadu Chennai India Computer Science Department Jamia Hamdard Delhi India University of Portsmouth Portsmouth United Kingdom System Analysis Control and Information Processing Dep Academy of Engineering RUDN University Moscow Russia The Institute of Industrial Management Economics and Trade Peter The Great Saint Petersburg Polytechnic University Russia

ISBN: (纸本)9781032669663

Enterprise-focused blockchain applications that offer safe, anonymous, and immutable transactions are growing as blockchain technology matures. Traditional blockchain architecture has low performance and scalability, making it unsuitable for enterpriselevel applications with high concurrency and massive data. This paper studies and proposes a simplified Byzantine fault-tolerant (SBFT) consensus algorithm to improve consensus stage efficiency, a Task parallel smart contract model to maximise multi-core system parallel efficiency, and an improved blockchain system architecture to reflect the lightweight, low coupling nature of blockchain technology. It facilitates corporate application secondary development and the ParaChain blockchain and smart contract technology. Experimental verification compares the parallelization - based ParaChain blockchain’s TPS and scalability. PBFT-based blockchain systems have been greatly improved. © 2024 The Author(s).

关键词： Smart contract

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Enhancing Short-Term Load Forecasting Accuracy in the Power systems Using a Hybrid CNN-GRU Model 5

Enhancing Short-Term Load Forecasting Accuracy in the Power ...

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5th International Conference on Electrical, Communication and computer Engineering, ICECCE 2024

作者： Ahsan, Muhammad Aziz, Muhammad Osama Bokhari, Syed Ahmad Saleem Haris, Muhammad Iqbal, Mansoor Ullah, Kaleem University of Alabama at Birmingham Department of Computer Science BirminghamAL35294 United States University of Engineering and Technology Buildings Energy Research Center Peshawar Pakistan Harvard Extension School Harvard University CambridgeMA02138 United States Institute for Insight - J Mack Robinson College of Business Georgia State University Georgia United States University of Wah Department of Computer Science Wah Cantt Pakistan CASE UET Peshawar Department of Electrical Energy System Peshawar Pakistan

ISBN: (纸本)9798331529437

In modern power systems, Short-Term Load Forecasting (STLF) is critical in ensuring the dynamic allocation of operational reserves and accurate prediction of electrical load demand. STLF is pertinent to grid operators to ensure system stability, allocate generation resources efficiently, and provide adequate reserves to deal with unexpected fluctuations. Considering the pivotal role of STLF, this paper introduces a hybrid deep learning model for short-term load forecasting, combining Convolutional Neural networks (CNN) and Gated Recurrent Units (GRU). The model features two parallel 32-filter convolutional layers (3×3 kernel, ReLU) for spatial feature extraction, followed by two series-connected GRU layers (42 units) to capture temporal dependencies. A final dense layer with ReLU activation generates the load forecast. This architecture effectively leverages CNNs for local pattern recognition and GRUs for modeling temporal sequences, offering a robust solution for accurate short-term load forecasting. Therefore, the efficacy of the CNN-GRU model is tested and validated using an extensive dataset sourced from the National Transmission & Dispatch Company (NTDC), Pakistan. The performance analysis reveals that hybrid CNN-GRU models achieve a substantial reduction in Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE), with values of 400.33, 2.89, 580.95, for single-step forecasts, respectively, and 1031.11, 5.31, and 733.19, for 24-hour forecasts. Furthermore, the simulation results demonstrate notable enhancements in load forecasting accuracy and robustness across various forecast horizons. © 2024 IEEE.

关键词： Convolutional neural networks

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EEG Data reduction with Epileptic Seizure Detection based machine learning in IoMT networks

EEG Data reduction with Epileptic Seizure Detection based ma...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Riyam Faisal Alwash Ali Kadhum Idrees Salah Al-Obaidi Department of Computer Science College of Science for Women University of Babylon Babylon Iraq Department of Information Networks College of Information Technology University of Babylon Babylon Iraq

There is a greater demand to enhance intelligent healthcare systems in order to keep track of the medical condition of patients. This demand leads to increase the interest in remote healthcare systems that make use of biosensors. These biosensors generate a significant amount of vital sensed data, which is then sent to the edge of the Internet of Medical Things (IoMT) for processing before being sent on to the data centers of the cloud. In this paper, we propose an EEG data reduction and epileptic seizures detection in IoMT networks. The suggested method operates on 2 levels within the network: edge and fog levels. At the edge gateway, The EEG data is transformed into time-frequency representations using Short Time Fourier Transformation (STFT). Then, a set of statistical texture feature based on the gray level co-occurrence matrix (GLCM) is extracted. These EEG based features are then compressed before being sent to the fog gateway using a binary-encoded representative method. In Fog Gateway, the received encoded features are decoded and passed into a classifier to determine the patient's situation. A set of classifiers are used to evaluate the proposed method using the Bonn University dataset. The proposed technique demonstrates the outperformance of the proposed method by achieving an accuracy of (100%) in multiple cases and an acceptable accuracy for other cases while considering fewer features compared to existing work.

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Thermal Image Features and Noise Effects Analysis 7

Thermal Image Features and Noise Effects Analysis

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7th International Engineering Conference "Research and Innovation Amid Global Pandemic", IEC 2021

作者： Salami, Ameen M. Salih, Dhiadeen M. Fadhil, Ahmed F. University of Kirkuk Iraq College of Computer Science and Info Technology Department of Computer Networks Kirkuk Iraq University of Kirkuk College of Engineering Department of Electrical Engineering Kirkuk Iraq

ISBN: (纸本)9780738132501

In this paper analysis are made to describe the effects of several types of noise on thermal image features like the Histogram, Texture, and Transform based features and compare it with regular image. The features are extracted from a bank of images before and after applying some noise bases like Gaussian, Salt Pepper, Speckle and the Poison on the images. The Signal to Noise Ratio and Mean Absolute Error were used as quality criteria to determine the noise consequence. The results showed that the Mode feature has an enormous rule in thermal images comparing to the regular images. However, the analysis display that the Homogeneity feature, which belongs to Texture features type, is the most affected feature after the types of noise are applied. On the other hand, the speckle noise showed the most impact on thermal image features compared to the regular images. © 2021 IEEE.

关键词： Signal to noise ratio

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A new symbol timing and carrier frequency offset estimation algorithm for OTFS for B5G 2

A new symbol timing and carrier frequency offset estimation ...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Thangavel, Gunasekaran Rajan, Sheeba Subbulekshmi, S. Anthonisamy, Arun Ganapathi, Rohini Velankanni, Gowri University of Technology and Applied Sciences Department of Electronics and Communication Engineering Muscat Oman SRM Institute of Science and Technology School of Computing Tiruchirapalli India Prathyusha Engineering College Department of Electronics and Communication Engineering Chennai India Panimalar Engineering College Department of Computer Science and Business System Chennai India St. Joseph's Institute of Technology Department of Electronics and Communication Engineering Chennai India K. Ramakrishnan College of Technology Department of Electronics and Communication Engineering Trichy India

ISBN: (纸本)9798331534950

In B5G networks, accurate symbol timing estimate and estimation of the carrier frequency offset are both absolutely necessary. Particularly in highly mobile and interference-ridden settings, these factors are of paramount importance for sustaining high-throughput, dependable wireless communication. Estimating symbol timing and carrier frequency offset in OTFS can be difficult due to factors including the time-varying nature of Doppler shifts and interference from surrounding cells as well as the non-stationary nature of wireless channels. In such changing settings, traditional algorithms often fail to produce reliable estimations. To account for frequency changes caused by things like Doppler effects and drift, this research uses Machine Learning-Based Carrier Frequency Prediction Analysis (ML-CFPA) to implement a dynamic carrier frequency offset estimation and correction system. To preserve signal coherence and lower interference in B5G settings, carrier frequency offset adjustment is essential. Communication solutions with high performance are required for these applications to ensure they can function normally despite the complexity and changeability of their contexts. The outcomes demonstrate the higher performance of the algorithm in contrast to conventional approaches, highlighting its applicability to B5G networks. Its potential as a crucial enabling technology for the B5G future has been solidified by these simulations, which verify its capacity to maintain reliable communication under a variety of real-world conditions. © 2024 IEEE.

关键词： Graphical user interfaces

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LoRaWAN and Biosensor-Assisted Smart City Framework to Mitigate the Effects of Lung Diseases

LoRaWAN and Biosensor-Assisted Smart City Framework to Mitig...

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2024 IEEE International Conference on Modeling, Simulation and Intelligent Computing, MoSICom 2024

作者： Abubeker, K.M. Roberts, Michaelraj Kingston Prajitha, C. Rinesh, S. Shafeena Karim, A. Jeevanandham, S. Department of Electronics and Communication Engineering Kerala India Sri Eshwar College of Engineering Centre for Advances in Signal Processing and Wireless Sensor Networks Department of Electronics and Communication Engineering Coimbatore India Karpagam Academy of Higher Education Centre for Interdisciplinary Research Department of Electronics and Communication Engineering Coimbatore India V.S.B College of Engineering Technical Campus Department of Computer Science and Engineering Coimbatore India Sri Ramakrishna Engineering College Department of Information Technology Coimbatore India

ISBN: (纸本)9798331533311

Urbanization and industrialization in cities present substantial obstacles to air quality, resulting in detrimental effects on public health, including respiratory health. This research introduces an Internet of Things (IoT) framework for a smart city environment using LoRaWAN and sensor networks. The framework utilizes the ESP32 LoRaWAN module to provide real-time monitoring of essential air quality metrics, such as carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), temperature, and humidity. Integrating biosensors enhances accuracy by identifying distinct biological indicators linked to respiratory well-being. The proposed work aims to use the framework in an urban setting within a global context, demonstrating its capacity to reduce the effects of lung disorders. The ESP32 LoRaWAN module enables effective communication within the sensor network up to 20 kilometers, guaranteeing a robust and adaptable solution for smart city applications. The developed framework provides a scalable and novel method for developing healthier and more sustainable cities, decreasing respiratory disorders as urban settings develop. © 2024 IEEE.

关键词： Lung cancer

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