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Feature Fusion Based Deep Transfer Learning Based Human Gait Classification Model

Intelligent Automation & Soft Computing 2023年第8期37卷 1453-1468页

作者： C.S.S.Anupama Rafina Zakieva Afanasiy Sergin E.Laxmi Lydia Seifedine Kadry Chomyong Kim Yunyoung Nam Department of Electronics and Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Candidate of Pedagogical Sciences Department of Industrial Electronics and Lighting EngineeringKazan State Power Engineering UniversityKazan420066Russia Department of Theories and Principles of Physical Education and Life Safety North-Eastern Federal University Named After M.K.AmmosovYakutsk677000Russia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of ICT Convergence Soonchunhyang UniversityAsan31538Korea

Gait is a biological typical that defines the method by that people *** is the most significant performance which keeps our day-to-day life and physical *** electromyography(sEMG)is a weak bioelectric signal that portrays the functional state between the human muscles and nervous system to any *** classifiers dependent upon sEMG signals are extremely utilized in analysing muscle diseases and as a guide path for recovery *** approaches are established in the works for gait recognition utilizing conventional and deep learning(DL)*** study designs an Enhanced Artificial Algae Algorithm with Hybrid Deep Learning based Human Gait Classification(EAAA-HDLGR)technique on sEMG *** EAAA-HDLGR technique extracts the time domain(TD)and frequency domain(FD)features from the sEMG signals and is *** addition,the EAAA-HDLGR technique exploits the hybrid deep learning(HDL)model for gait *** last,an EAAA-based hyperparameter optimizer is applied for the HDL model,which is mainly derived from the quasi-oppositional based learning(QOBL)concept,showing the novelty of the work.A brief classifier outcome of the EAAA-HDLGR technique is examined under diverse aspects,and the results indicate improving the EAAA-HDLGR *** results imply that the EAAA-HDLGR technique accomplishes improved results with the inclusion of EAAA on gait recognition.

关键词： Feature fusion human gait recognition deep learning electromyography signals artificial algae algorithm

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Evaluating LSTM and GRU Models for Cryptocurrency Price Forecasting in Financial Markets 3

Evaluating LSTM and GRU Models for Cryptocurrency Price Fore...

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3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Mehra, Isha Tyagi, Manoj Kumar Kanthalia, Shilpa Shree, Tanvi Chaturvedi, Rajnish Kumar Singh, Prabhishek ABES Engineering College Department of Applied Sciences & Humanities Ghaziabad India School of Computer Science and Engineering Galgotias University India Faculty of Management Studies Udaipur India Raj Kumar Goel Institute of Technology Department of Information Technology Ghaziabad India School of Computer Science Engineering and Technology Bennett University India

ISBN: (纸本)9798350342338

Cryptocurrencies are a type of digital money distinguished by a decentralized system that uses encryption to authenticate transactions and keep records, obviating the need for a central authority. A key element of these digital assets is the decentralization of power from a single entity to a dispersed network. The extreme price volatility of cryptocurrencies, on the other hand, has a significant influence on international commerce, making precise price forecasting critical for investors and traders. In this study, the investigation is made on how deep learning models, especially the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), may be used to solve the problem of excessive price swings in cryptocurrencies like as Bitcoin, Ethereum, Litecoin, and Dogecoin. Previous research has looked at forecasting methods such as ARIMA and Support Vector Machines (SVM), but the findings have fallen short of the promising results obtained by the deep learning models studied in this work. Our major objective is to create strong forecasting models based on LSTM and GRU, with an emphasis on their ability to make credible forecasts for bitcoin values. We find that GRU consistently beats LSTM for the majority of the cryptocurrencies under consideration by comparing their performance using two separate error prediction approaches, namely mean absolute percentage error (MAPE) and root mean square error (RMSE). This study's findings help to enhance prediction methods for understanding and reducing the impact of cryptocurrency price volatility on international commerce. Deep learning algorithms for projecting bitcoin values give significant insights for investors and decision-makers navigating the volatile and ever-changing terrain of the crypto market. © 2023 IEEE.

关键词： IoT machine learning performance metrics security attacks

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Exploration of Ripe Raw and Defective Tomatoes with Deep Learning Techniques 2

Exploration of Ripe Raw and Defective Tomatoes with Deep Lea...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Vanitha, C.N. Sathyamoorthy, Malathy Dhanaraj, Rajesh Kumar Rai, Priti Geetha, V. Manikantan, M. Kongu Engineering College Science and Engineering Department of Computer Erode India Kpr Institute of Engineering and Technology Department of Information Technology Coimbatore India Pune India Gl Bajaj Institute of Management Greater Noida India Reva University School of Applied Sciences Department of Computer Science Bangalore India Kongu Engineering College Dept. of CT-UG Perundurai India

ISBN: (纸本)9798350325799

Due to great need of agricultural operations, there was a shortage of workers during the harvest period. This results in a bottleneck in the operations of agricultural organizations. One of the most common problems in these sectors is the hazardous practice of farming. This type of farming operation makes vegetables very costly and unattractive. The reduction of costs is controlled by the need for labour. This issue is solved by adapting to new technology and compensating the labour deficiency. The algorithms that are processed in this method are Convolutional Neural Network (CNN), Object Detection and Generative Adversarial Network (GAN). These algorithms will enable the model to recognise faulty tomatoes. This model accurately identifies raw, ripe and defective tomatoes using deep learning methods and it is highly helpful for the betterment of agriculture. © 2023 IEEE.

关键词： Object detection

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Intelligent Digital Twin Communication Framework for Addressing Accuracy and Timeliness Tradeoff in Resource-Constrained Networks

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IEEE Transactions on Cognitive Communications and Networking 2024年第3期11卷 1954-1965页

作者： Cakir, Lal Verda Thomson, Craig J. Ozdem, Mehmet Canberk, Berk Nguyen, Van-Linh Duong, Trung Q. Edinburgh Napier University School of Computing Engineering and Built Environment EdinburghEH10 5DT United Kingdom BTS Group Istanbul Turkey Turk Telekom Turkey Department of Computer Science and Information Engineering Taiwan Memorial University of Newfoundland Faculty of Engineering and Applied Science St. John'sNLA1C 5S7 Canada Queen's University Belfast School of Electronics Electrical Engineering and Computer Science BelfastBT7 1NN United Kingdom

The accuracy and timeliness tradeoff prevents Digital Twins (DTs) from realizing their full potential. High accuracy is crucial for decision-making, and timeliness is equally essential for responsiveness. Therefore, this tradeoff in DT communication must be addressed to achieve DT synchronization. Previous studies identified the issue but considered the problem as maximizing data transfer, which is infeasible due to resource constraints. To facilitate this, we quantify accuracy and timeliness as E and φ and define the problem as joint minimisation. We then introduce the Intelligent DT Communication (IDTC) Framework to solve the problem, which includes machine learning-based Predictive Synchronization (PS) and DT synchronization management (DTSYNC) protocol. Here, PS uses imputation and forecasting to generate future values, which are utilized to update DT at the projected time points. This mechanism of PS enables lowering E and φ of the communication. Subsequently, we utilize the DTSYNC to control synchronization and optimise the twining frequency ft. We evaluate the proposed framework using a public dataset and compare its performance with several state-of-the-art studies in a real-world scenario. Evaluation results indicate that IDTC outperforms the existing methods by 80% for E and 84% for φ while enabling ft adjustment, resulting in 3.8 times goodput improvement. © 2015 IEEE.

关键词： Adversarial machine learning

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Securing Smart Homes Using Hybrid Stacking Ensemble Deep Learning-Enabled Framework 2

Securing Smart Homes Using Hybrid Stacking Ensemble Deep Lea...

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2nd International engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Moubayed, Abdallah Injadat, MohammadNoor Abdellatif Mohamed, Tamer Mohamed Almatarneh, Sattam Al-Mashagbeh, Malak Aljaidi, Mohammad School of Computing and Augmented Intelligence Arizona State University GilbertAZ United States Lebanese American University Electrical and Computer Engineering Department Byblos Lebanon Western University Electrical and Computer Engineering Department LondonON Canada Zarqa University Data Science and Artificial Intelligence Department Zarqa Jordan School of Engineering Applied Science and Technology Canadian University Dubai Dubai United Arab Emirates Zarqa University Cyber Security Department Zarqa Jordan Zarqa University Computer Science Department Zarqa Jordan

ISBN: (纸本)9798350373363

The added deployment of Internet-of-Things (IoT) devices in various environments has led to people increasingly adding this technology to their homes to make them more comfortable and have a more convenient living environment. Despite the promise and the growth potential of the smart home market, one main concern is the security and privacy of smart home devices. This is because smart home devices equipped with IoT technology are prone to various network-based attacks. With smart home devices having limited computation and memory capabilities, many IoT manufacturers often overlook the security aspect by focusing more on the functionality. To address this issue, the concepts of machine learning (ML) and deep learning (DL) have emerged as viable and promising solutions. To that end, this paper proposes a hybrid stacking ensemble DL-enabled framework that combines both classical ML models with sophisticated DL models. The goal is to make use of the simplicity and interpretability of the classical ML models with the superior time-dependent patterns that DL models can detect. The proposed framework's performance is evaluated using multiple datasets representing different smart home devices. Experimental results show that the proposed framework achieved better detection accuracy, precision, recall, and F-score. © 2023 IEEE.

关键词： Intrusion detection

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Deep learning-based cognitive digital twin system for wrist pulse diagnostic and classification 2nd

Deep learning-based cognitive digital twin system for wrist ...

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2nd International Conference on Next-Generation Computing and Information Systems, ICNGCIS 2023

作者： Chaudhary, Jitendra Kumar Parimalam, T. Alghayadh, Faisal Yousef Keshta, Ismail Soni, Mukesh Degadwala, Sheshang School of Computing Graphic Era Hill University Bhimtal Campus Uttrakhand India Department of Computer Science Nandha Arts and Science College Tamilnadu Erode India Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Dr. D. Y. Patil Vidyapeeth Pune Dr. D. Y. Patil School of Science & Technology Tathawade Pune India Department of Computer Engineering Sigma University Gujarat Vadodara India

ISBN: (纸本)9781032738659

In analyzing and recognizing wrist pulse signals, it isn’t easy to mine the nonlinear information of wrist pulse signals using analysis methods such as time and frequency. Traditional machine learning methods require the manual definition of features and cannot perform self-learning of features. A cognitive digital twin technique for pulse analysis and recognition based on threshold-less recursive graph and CNN is proposed. The wrist pulse signal is converted into a threshold-free recursive graph based on the nonlinear dynamics’ theory. The VGG-16 CNN automatically extracts the nonlinear features of the recursive graph, and a pulse condition classification model is established. Experimental finding several that the classification of the proposed method accuracy can reach 98.14%, as compared with the existing pulse classification methods. This study offers a novel concept and strategy for classifying pulse signals, and it has application to the objectification of pulse diagnosis. © 2025 The Author(s).

关键词： Graphic methods

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Research on Cancer Prediction and Identification based on Multimodal Medical Image Fusion 24

Research on Cancer Prediction and Identification based on Mu...

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3rd International Symposium on Robotics, Artificial Intelligence and Information engineering, RAIIE 2024

作者： Diao, Su Wan, Yajie Huang, Shijia Ma, Haowei Department of Industrial & Systems Engineering Auburn University Alabama36849 United States Department of Computer Science Brown University ProvidenceRI02912 United States Fu Foundation School of Engineering and Applied Science Columbia University New York10027 United States Department of Mechanical and Aerospace Engineering Case Western Reserve University ClevelandOH44106 United States

ISBN: (纸本)9798400718311

Multimodal medical image fusion plays a crucial role in computer-aided medical diagnosis, especially in the field of medical image research. Different medical images of the same cell, tissue, bone, organ, and system often show different imaging results due to differences in imaging parameters and mechanisms between different imaging techniques. As a result, single-modal medical images often have limited information and are difficult to provide comprehensive diagnostic information. Multimodal medical image fusion technology has been widely used in the prediction and recognition of cancer, improving the accuracy and efficiency of diagnosis. In this study, a new cancer prediction and recognition method was developed based on multimodal medical image fusion technology. Firstly, the deep learning algorithm is used to integrate data from multiple imaging modes to extract key features through an optimized convolutional neural network model. Secondly, an improved image registration technology is introduced to ensure the accurate alignment between images of different modes, so as to enhance the comprehensive expression ability of image data. Finally, the fused image data was classified to achieve efficient prediction and identification of cancer. Experimental analysis shows that the proposed method is superior to the traditional single-modal image analysis method in terms of accuracy and sensitivity of cancer identification. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Convolutional neural networks

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Improving Existing VANETs by Incorporating UAVS Using Ant Colony and PSO Algorithms 4

Improving Existing VANETs by Incorporating UAVS Using Ant Co...

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4th IEEE International Conference on Data engineering and Communication Systems, ICDECS 2024

作者： Thangam, S. Dixit, Aniket Karn, Alok Kumar Thakur, Arnav Arunbalaji, Cg Jesy Janet Kumari, J. Saleem Raja, A. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India University of Technology and Applied Sciences-Shinas College of Computing and Information Sciences It Department Oman

ISBN: (纸本)9798350393354

Vehicular Ad Hoc Networks (VANETs) are a type of wireless communication network that enable vehicles to communicate with each other and with roadside infrastructure in a peer-to-peer fashion;despite their vast applications, they come with several shortcomings. The key challenges in their real-world implementation include scalability, connectivity, and coverage issues. Unmanned Aerial Vehicles (UAVs), also known as drones, can complement existing VANETs in several ways to enhance their functionality and address some of their limitations. This paper studies the incorporation of UAVs in VANETs to overcome the challenges faced by the present networks. The approach is based on the dynamic deployment of UAVs in the most optimal positions, found by utilizing Particle Swarm Optimization (PSO) and Ant Colony algorithms which analyze the vehicle density, and previous coverage information in the network. The deployment of UAVs is intended to provide a seamless network coverage for ground vehicles. The impact of dynamic UAV mobility in communicating VANETs is comparatively studied. The simulation is done using Network Simulator-3 (NS3) simulator to evaluate the performance of 4 VANET protocols, AODV, DSR, OLSR, and DSDV, in terms of packet delivery ratio (PDR), Average End-to-End Delay, Throughput, Average Throughput per Packet, Packet Drop Rate, and Normalized Routing Load after incorporating the proposed modification designs. The paper concludes that the incorporation of Ant Colony is better suited to enhance VANETs than PSO. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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A Visual Attention-Based Model for Bengali Image Captioning

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SN computer science 2023年第2期4卷 208页

作者： Das, Bidyut Pal, Ratnabali Majumder, Mukta Phadikar, Santanu Sekh, Arif Ahmed Department of Information Technology Haldia Institute of Technology West Bengal Haldia 721657 India Department of Applied Mathematics NIT Durgapur West Bengal Durgapur 713213 India Department of Computational Science Brainware University West Bengal Kolkata 700125 India Department of Computer Science and Technology University of North Bengal West Bengal Siliguri 734014 India Department of Computer Science and Engineering Maulana Abul Kalam Azad University of Technology W.B. West Bengal Haringhata 741249 India School of Computer Science and Engineering XIM University Odisha Bhubaneswar 752050 India

Image caption or description generation is a fundamental task that involves computer vision (CV) and natural language processing (NLP) ideas to recognize an image-context and produces description(s) using a natural language. Bengali is one of the world’s most commonly spoken languages, ranking fifth. For this reason, large research achievements have been recognized to image captioning, i.e. explaining images with grammatically correct and semantically meaningful Bengali sentences. Many established datasets exist for image caption generation in English, but no standard dataset is available for Bengali. This paper proposes a model for generating automatic image captions in the Bengali language. This study uses only two initial available Bengali datasets to train the encoder-decoder neural network model. We have curated the human errors available in the datasets and benchmarked. Experimental results of this proposed model performs better than other baseline models using these datasets. It achieved 0.67 and 0.65 BLEU-1, and 0.26 and 0.24 BLEU-4 respectively. We expect that our research will take attentions to more researchers from regional language understanding and accelerate Bengali vision-language understanding. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Attention mechanism Bengali image caption Bengali NLP Deep learning Image description

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Enhancing Acute Ischemic Stroke Diagnosis Using IoMT and Deep Learning Technologies

Enhancing Acute Ischemic Stroke Diagnosis Using IoMT and Dee...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： El-Sheikh, Mohamed M. Bayoumy, Aya Ahmed, Nada M. Mohamed Eldeeb, Amany Rabea, Fatma I. Nawar, Amira A. Khattap, Mohamed G. Galal Eldeen Mohamed Ali Hassan, Hend Mostafa, Mariam Ibrahim, Nayra Ramadan, Reem Refaat, Yomna Khaled, Mennatallah Maher, Ali Abd Elaziz, Mohamed Gamal Abdellatif Ibrahim, Ahmed Radiology and Medical Imaging Faculty of Applied Health Sciences Galala University Suez435611 Egypt Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Artificial Intelligence Science Program Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Air Defense College Department of Electronics and Electrical Communications Engineering Alexandria Egypt

ISBN: (纸本)9798350351408

This paper introduces an alternative technique for diagnosing Acute Ischemic Stroke within the IoMT environment. In the proposed approach, the collected data is transmitted to a cloud-based center where the technique utilizes EfficientNet, a deep learning model, designed to extract features from MRI images thereby enhancing the detection of acute ischemic infarctions. The performance of EfficientNet is compared against two other models, CNN and MobileNet, demonstrating its superior efficacy through metrics such as accuracy, precision, recall, and F1-score, which stand at 92.31%, 92.28%, 92.33%, and 92.30%, respectively. © 2024 IEEE.

关键词： Contrastive Learning

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