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1st International Conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Hashim, Mohammed Mahdi Al-Hilali, Aqeel A. Qasim, Haitham Bashar Salah, Omar Raad Nahi, Abdullah A. Ministry of Higher Education and Scientific Research Baghdad Iraq Al-Farahidi University Department of Medical Instruments Engineering Techniques Baghdad Iraq AL-Esraa University College of Science Department of Computer Science Baghdad Iraq Al-Hadba University College Department of Computer Technology Engineering Mosul Iraq Management Technical College Southern Technical University Department of Information Technologies Basrah Iraq

ISBN: (纸本)9798350342796

The inconceivable volume of online images produced by websites and personal collections has made it difficult to retrieve images from vast databases accurately. Practically, consumers need help to get the relevant information they seek. Humans are able to identify images according to their apparent characteristics. In addition, the image pixels cannot offer enough detail for creating semantic ideas, rendering image annotation a difficult endeavor. A particularly more challenging assignment in computer Vision (CV) and multimedia studies is image annotation (IA), in which the method assigns keywords from the dictionary to a given digital image. The major aim of IA is to convert the given digital image into single or different labels. To address this issue, this study focused on the extraction of automatic features to improve image annotation. Different techniques have been used with the suggested method such as YCbCr, Local Binary Pattern (LBP), and Slantlet Transform (ST) to improve a Feature Extraction (FE). In addition, the most widely used datasets, such as Flickr8k, ESP-Game, and Corel-5k, were used to test the proposed study. The evaluation results demonstrated superior annotation flexibility, enhanced accuracy, and decreased computation cost. © 2023 IEEE.

关键词： computer Vision Conventional Neural Network Image annotation Local Binary Pattern Slantlet Transform

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Identifying Inaccurate Stories: Leveraging High Knowledge and Big Data 15

Identifying Inaccurate Stories: Leveraging High Knowledge an...

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15th International Conference on Computing Communication and Networking technologies, ICCCNT 2024

作者： Khan, Mohammad Ahmar Verma, Pushpendra Kumar Ghaldiyal, Shivangi Balaji, N.V. Francis, G. Arun Patil, Swati Bula Department of Management Information System College of Commerce & Business Administration Dhofar University Country Sultanate of Oman Oman School of Computer Science and Applications IIMT University Meerut India Department of Computer Applications Invertis University Uttar Pradesh Bareilly India Department of Computer Science Karpagam Academy of Higher Education Tamil Nadu Coimbatore India Department of Electronics and Communication Engineering Karpagam College of Engineering Tamil Nadu Coimbatore India IT Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9798350370249

Research on fake news has gained significant traction in a number of fields, We describe how the issue is tackled from a natural language processing standpoint in this research with the aim of developing identify false material in news. Collecting high-quality data, such as examples of false and actual news stories on a diverse range of subjects, is the key obstacle in this field of study. We examine the available before introducing the site as our lab's gift to the . We provide the whole text of the news that have already been applied based on human evaluation of the articles' authenticity. In order to clarify the gaps and causes of imbalance in the datasets that are now accessible and to direct ongoing attempts, we also conduct an appearance-based experiment. We urge the public to gather more information and make it accessible for study. ©2024 IEEE.

关键词： Data collection

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Multimodal Neuroimaging for Early Alzheimer's detection: A Deep Learning Approach 14

Multimodal Neuroimaging for Early Alzheimer's detection: A D...

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14th International Conference on Computing Communication and Networking technologies, ICCCNT 2023

作者： Singh Chhabra, Gurpreet Guru, Abhishek Rajput, Bhawna Janghel Dewangan, Leelkanth Swarnkar, Suman Kumar Gitam Deemed to Be University Computer Science & Engineering Department of Gitam School of Technology Visakhapatnam India Koneru Lakshmaiah Education Foundation Department of Cse AP Vaddeswaram522302 India Rungta College of Engineering in Bhilai Department of Computer Science & Engineering C.G. Durg India G H Raisoni Institute of Engineering and Business Management Maharashtra Jalgaon India Shri Shankaracharya Institute of Professional Management and Technology Department of Computer Science & Engineering Chhattisgarh Raipur India

ISBN: (纸本)9798350335095

The timely identification and treatment of Alzheimer's detection (AD) is of paramount importance. The present study introduces a deep learning methodology for the timely detection of Alzheimer's detection through the utilization of multimodal neuroimaging data. The authors integrated sMRI, fMRI, and DTI modalities to generate a comprehensive set of features. Subsequently, a pioneering deep learning architecture was developed to consolidate and evaluate these characteristics with the aim of Alzheimer's detection categorization. The dataset utilized in this study comprised of 1200 participants, with 600 individuals diagnosed with early-stage AD and 600 healthy controls matched for age. The application of data augmentation techniques was employed to mitigate class imbalance and enhance the resilience of our model. We used a three-tiered deep learning architecture, which included a sMRI CNN, an fMRI RNN, and a DTI GCN to analyze MRI data. The final categorization was performed by fusing several previously separate networks into a single multimodal neural network. Our study involved a comparative analysis of the efficacy of our model vis-a-vis conventional machine learning algorithms and pre-existing deep learning techniques. Classification accuracy of 93.5%, sensitivity of 92.3%, and specificity of 94.6% were all attained by our suggested method, which represents state-of-the-art performance. Additionally, the model exhibited enhanced generalizability upon evaluation with an external dataset, suggesting its viability for employment in clinical settings. The utilization of multimodal neuroimaging data through our deep learning approach presents a promising avenue for the timely detection of Alzheimer's detection. The results of our study indicate that the amalgamation of sMRI, fMRI, and DTI information can notably augment the precision of diagnosis, thereby opening up avenues for individualized therapeutic approaches in individuals with Alzheimer's detection. Subsequent

关键词： Recurrent neural networks

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Hybrid Deep Learning Enabled Air Pollution Monitoring in ITS Environment

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computers, Materials & Continua 2022年第7期72卷 1157-1172页

作者： Ashit Kumar Dutta Jenyfal Sampson Sultan Ahmad T.Avudaiappan Kanagaraj Narayanasamy Irina V.Pustokhina Denis A.Pustokhin Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626128India Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj 11942Saudi Arabia Department of Computer Science and Engineering K.Ramakrishnan College of TechnologyTiruchirappalli621112India Department of Computer Applications J.J College of Arts and Science(Autonomous)Affiliated to Bharathidasan UniversityPudukkottai622422India Department of Entrepreneurship and Logistics Plekhanov Russian University of Economics117997MoscowRussia Department of Logistics State University of Management109542MoscowRussia

Intelligent Transportation Systems(ITS)have become a vital part in improving human lives and modern *** aims at enhancing road safety and environmental *** is a tremendous increase observed in the number of vehicles in recent years,owing to increasing *** vehicle has its own individual emission rate;however,the issue arises when the emission rate crosses a standard *** to the technological advances made in Artificial Intelligence(AI)techniques,it is easy to leverage it to develop prediction approaches so as to monitor and control air *** current research paper presents Oppositional Shark Shell Optimization with Hybrid Deep Learning Model for Air Pollution Monitoring(OSSOHDLAPM)in ITS *** proposed OSSO-HDLAPM technique includes a set of sensors embedded in vehicles to measure the level of *** addition,hybridized Convolution Neural Network with Long Short-Term Memory(HCNN-LSTM)model is used to predict pollutant level based on the data attained earlier by the *** HCNN-LSTM model,the hyperparameters are selected and optimized using OSSO *** order to validate the performance of the proposed OSSO-HDLAPM technique,a series of experiments was conducted and the obtained results showcase the superior performance of OSSO-HDLAPM technique under different evaluation parameters.

关键词： Deep learning air pollution environment monitoring internet of things intelligent transportation systems oppositional learning LSTM model

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Study on the Applicability of Deterioration Detection Techniques for Sandstone Heritage in Multi Environment using MLP-Attention Model

Study on the Applicability of Deterioration Detection Techni...

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2024 Symposium on Spatial Information to Empower the Metaverse

作者： Yan, Chengjin Cheng, Yuan Zhang, Yue Yu, Haomin Huang, Jizhong Yan, Hongbin Ministry of Education China School of Computer Engineering and Science Shanghai University 99 Shangda Road Shanghai200444 China Institute for the Conservation of Cultural Heritage School of Cultural Heritage and Information Management Shanghai University Shanghai China Department of Computer Science Aalborg University Aalborg Denmark Yungang Research Institute Datong China

Stone cultural heritage, encompassing a broad spectrum of artifacts such as stone artworks, buildings, tools, and utensils, represents one of the most significant categories of cultural heritage. However, the conservation of these cultural heritage faces challenges from the process of deterioration. This degradation not only compromises the structural integrity of the heritage but also results in the loss of invaluable historical information. Thus, there emerges a critical demand for effective methods to detect and assess the condition of stone cultural heritage, enabling timely and precise conservation interventions. Here we obtained sandstone samples under different deteriorative environments through laboratory-simulated deterioration experiments and employed a variety of detection technologies to capture a series of changes in the deterioration detection parameters during the sandstone deterioration process. Subsequently, a deep learning model was established to correlate the detection parameters with the degree of stone deterioration. A SHAP analysis was then conducted to determine the contribution of various parameters to the degree of stone weathering under different experimental environments, providing recommendations for selecting appropriate detection technologies and indicators adapted to different deterioration environments. To further analyze the deterioration processes of the stone samples, XRD analysis was conducted to observe changes in mineral composition throughout the deterioration process. SEM images were utilized to examine the changes in micro-morphology and the internal pore structure associated with deterioration. This study provides a basis for the scientific design of deterioration detection schemes by selecting the most suitable testing technology for optimal deterioration assessment under specific environmental conditions. © Author(s) 2024.

关键词： Weathering

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Efficient Novel Method for EEG Signal Classification in Epileptic Seizure Identification using Metaheuristic Deep Learning

Efficient Novel Method for EEG Signal Classification in Epil...

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2024 IEEE International Conference on Recent Advances in science and Engineering Technology, ICRASET 2024

作者： Pokkuluri, Kiran Sree Nigam, Nishesh Fuskele, Avita Raja, C. Shukla, Prashant Kumar Chhaya Shri Vishnu Engineering College for Women Department of Computer Science and Engineering Bhimavaram India Ies Institute of Technology and Management Ies University Department of Computer Science and Engineering MP Bhopal462044 India Jabalpur Engineering College Information Technology Department Jabalpur India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Tamil Nadu Chennai India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram Guntur India Mangalayatan University Jabalpur India

ISBN: (纸本)9798350388602

Using a metaheuristic deep learning strategy, the research focuses on the challenge of epileptic seizure detection. A diagnostic tool known as an electroencephalogram (EEG) may detect improperly functioning brain cells, the underlying cause of seizures. To enhance the precision of seizure detection, the offered approach merges deep learning with a metaheuristic optimization technique. This method involves preprocessing raw EEG data in order to extract fundamental characteristics. A classifier that is part of a deep learning system is subsequently fed these characteristics in stages. When it comes to finding a decent solution with high accuracy dimensions, the metaheuristic component finds the optimal values for the deep learning model. Due to the sluggish convergence and trapping of local minima in standard clustering methods, this approach seeks to improve detection performance. Using industry standard EEG datasets, we evaluate our hybrid technique's performance to that of other relevant models. These findings demonstrate that the system is a dependable tool for real-time seizure detection, with sensitivity, specificity, and accuracy all surpassing the current standards. This method stands head and shoulders above its predecessors, proving its viability for clinical usage in epilepsy monitoring and diagnosis. © 2024 IEEE.

关键词： Electroencephalography

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Multi-Criteria Decision-Making for Energy management in Smart Homes Using Hybridized Neuro-Fuzzy Approach

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Distributed Generation and Alternative Energy Journal 2024年第1期39卷 83-109页

作者： Anbazhagu, U.V. Koti, Manjula Sanjay Muthukumaran, V. Geetha, V. Munrathnam, Meram Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Kattankulathur TN Chennai India Department of MCA Dayananda Sagar Academy of Technology and Management Bangalore560082 India Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur Campus 603 203 India Department of Computer Science School of Applied Sciences REVA University Bangalore India Department of Mathematics Rajiv Gandhi University of Knowledge Technologies R.k.Valley Idupulapaya Andhra Pradesh Kadapa516330 India

The necessity for smart energy oversight solutions has arisen in response to the rising popularity of energy-efficient home automation and other energy-saving technologies. Optimizing smart home energy use using multi-criteria decision-making (MCDM) is a proven methodology. However, the procedure for making decisions and MCDM’s capacity to handle various criteria are typically limiting factors. Hybrid methods, which integrate multiple decision-making approaches like Fuzzy Logic (FL) and Modular Neural Networks (MNN), could potentially be able to circumvent these restrictions and boost energy management systems’ efficacy and precision. This investigation presents a hybrid Neuro-Fuzzy (H-NF) method for MCDM in regulating energy for smart homes by combining FL with an MNN. The suggested approach would optimize energy use in smart homes by considering several parameters, notably cost, ease of use, and environmental effects. In addition, this study aims to examine how the H-NF model fares in comparison to other methods of making important decisions in terms of several performance metrics. The suggested hybridized approach has the potential to deliver more precise and effective decision-making processes for energy management in smart homes, allowing users to optimize their energy consumption while preserving comfort and lowering environmental impact. © 2024 Taylor and Francis Inc.. All rights reserved.

关键词： Decision making

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A Colored Petri Net Tool for the Design of Robotic Palletizing Cells 9

A Colored Petri Net Tool for the Design of Robotic Palletizi...

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9th International Conference on Control, Decision and Information technologies, CoDIT 2023

作者： Cavone, Graziana Stella, Silvia Scarabaggio, Paolo Carli, Raffaele Lisi, Stefano Garavelli, Achille Claudio Dotoli, Mariagrazia University Roma Tre Department of Civil Computer Science and Aeronautical Technologies Engineering Italy Polytechnic of Bari Department of Mechanics Mathematics and Management Italy Polytechnic of Bari Department of Electrical and Information Engineering Italy

ISBN: (纸本)9798350311402

Driven by the digital transformation required by Logistics 4.0, the use of automation in warehouses is constantly growing. In particular, robotic palletizers offer significant potential for optimizing warehouse operations, thanks to higher flexibility and throughput than traditional palletizing systems. Despite the availability of several solutions in the market, the optimal deployment of a robotic palletizer in warehouses is not straightforward: a design phase is needed to determine the most convenient configuration that ensures automatic palletizing is fully integrated into the warehouse processes. In this paper, we propose a simulation-based versatile tool for modeling and analysis purposes, aimed at supporting the design of a robotic palletizing cell in a bottom-up fashion. As a core methodology, we employ timed colored Petri nets, which allow - once the analysis on packing requirements and constraints is conducted - to rapidly model the system as a composition of basic subsystems, and implement alternative simulations to evaluate the corresponding performance and effectively benchmark the alternative configurations. The proposed approach is applied to a real case study, showing its effectiveness in identifying the solution that achieves a good compromise between the use of resources and the performance of warehouse operations. © 2023 IEEE.

关键词： Discrete event simulation

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An innovation Proving of Knowledge Bases for Automated Reasoning for Information Analysis 3

An innovation Proving of Knowledge Bases for Automated Reaso...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Chanthati, Sasibhushan Rao Pund, Sachin S. Ghosh, Suvasis Lal, Bechoo Kaushal, Ashish Ammisetty, Veeraswamy T. Rowe Price Harrisburg University of Science and Ttechnology United States Shri Ramdeobaba College of Engineering and Management Department of Industrial Engineering Maharashtra Nagpur India Kiit School of Management Department of School of Management Bhubaneswar India Konenru Lakshmaiah Education Foundation Department of Computer Science and Engineering AP Vaddeswaram India Jindal Global Business School O P Jindal Global University Haryana Sonipat India KoneruLakshmaiah Education Foundation Department of Computer Science Engineering College University Vaddeswaram India

ISBN: (纸本)9798350319125

This paper examines the capability of using information Bases (KBs) in automatic reasoning for superior facts analysis. KBs, as a form of information representation, are uniquely suited for automated reasoning as they shop various kinds of domain-specific statistics in a single pool of declarative facts. The paper explores how to reliably and efficiently use such declarative data shops connecting one of a kind forms of artificial evidence and techniques. Right here, a new technique is proposed that unites the efficiency of RDF and the expressiveness of text-primarily based documents for representing the research context. An extension of the RDF format to represent reasoning steps and knowledge entities extra as it should be is proposed and tested on a repository of actual-global clinical and genetics datasets. The outcomes prove that the approach can improve computation of diverse components of studies workflow consisting of gene-disorder mapping tasks, whilst retaining affordable computational complexity. The proposed KB extends the capacity to generate efficient and powerful records analysis for research contexts and may result in new paradigms of automated reasoning. © 2023 IEEE.

关键词： computation consisting efficiently repository represent

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Retrainable Hand Gesture Recognition Model Driven by Edge AI

Retrainable Hand Gesture Recognition Model Driven by Edge AI

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International Conference on Dependable Systems and Their Applications (DSA)

作者： Tse-Chuan Hsu Dong-Meau Chang Department of Computer Science & Information Management Soochow University Taipei Taiwan School of Computer Science and Intelligence Education Lingnan Normal University China

ISBN: (数字)9798331532390

ISBN: (纸本)9798331532406

Based on computer vision and machine learning, accurate hand behavior and gesture recognition are crucial, especially for missing or incomplete hand models. Finger joint point prediction technology is of great value for applications such as virtual reality and human posture recognition. However, hand joint point prediction models, such as Google’s MediaPipe, perform well in full-finger models but are ineffective in missing-finger situations. Missing fingers are familiar in clinical medicine, rehabilitation facilities, and unique scenarios such as hand injuries or congenital hand deformities. Accurate prediction of hand joints is critical for hand rehabilitation, prosthetic design, and human motion analysis and inspires the development of related technologies. Since traditional prediction models rely on complete hand data, and missing fingers can make the hand structure irregular, the complexity of prediction increases. This study proposes an improved prediction model focusing on hand joint point prediction in the case of missing fingers, hoping to improve prediction accuracy and provide valuable reference for future applications and research. Among the research results, we successfully integrated the new training image model with MediaPipe, significantly improving missing fingers’ prediction accuracy.

关键词： Hands Training Solid modeling Adaptation models Accuracy Computational modeling Gesture recognition Virtual reality Predictive models Data models

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