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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Smitha, B.S. Jain, Alok Sreenivasulu, D. Sharma, Saurabh Reddy, B. Ravali Al-Rubaye, Taqi Mohammed Khattab New Horizon College of Engineering Department of Automobile Engineering Bangalore India Lovely Professional University Phagwara India Institute of Aeronautical Engineering Dundigal Hyderabad500043 India Lloyd Law College Plot No. 11 Knowledge Park II Uttar Pradesh Greater Noida201314 India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India The Islamic University College of Medical Technologies Department of Medical Laboratory Technology Najaf Iraq

ISBN: (纸本)9798350350067

As computer vision systems have improved, mostly through machine vision progress, people's views on pictures have changed a lot. In response to this change, the Dynamic AdaptNet project is making an advanced system for analysing pictures that uses data from the surroundings. The manuscripts goals is to offer a full range of features, such as dynamic convolutional neural networks (CNNs), adaptable segmentation, and temporal attention. Standard, Baseline, Enhanced, Advanced, Optimised, and Specialised are some of the choices for the system. Standard is the easiest and most important to use. This study compares and contrasts different methods by checking how well they work based on several factors, such as success, adaptability, and resilience. Independent reviews and comparisons show that Dynamic AdaptNet is a complete tool for real-time picture analysis. This shows that it has the ability to make computer vision better. © 2024 IEEE.

关键词： Convolutional neural networks

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IoT Enabled Bee Hive Monitoring System 33

IoT Enabled Bee Hive Monitoring System

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33rd IEEE International Conference on Microelectronics, MIEL 2023

作者： Milovanovic, M. Pejic, J. Pejic, P. University of Niš Faculty of Electronic Engineering Department of Electronics Niš18000 Serbia University of Niš Faculty of Sciences and Mathematics Department of Computer Science Niš18000 Serbia Belgrade Metropolitan University Faculty of Information Technology Serbia

Beehive monitoring can provide useful information for people associated with beekeeping to help to manage their honey bee (Apis mellifera) colonie. This paper presents the development and experimental results of a IoT... 详细信息

ISBN: (纸本)9798350347760

关键词： Internet of things

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Graph Transformer for Communities Detection in Social Networks

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computers, Materials & Continua 2022年第3期70卷 5707-5720页

作者： G.Naga Chandrika Khalid Alnowibet K.Sandeep Kautish E.Sreenivasa Reddy Adel F.Alrasheedi Ali Wagdy Mohamed Department of Computer Science and Engineering ANU College of Engineering and TechnologyGuntur522510India Statistics and Operations Research Department College of ScienceKing Saud UniversityRiyadh11451Kingdom of Saudi Arabia LBEF Campus Kathmandu44600Nepal Department of Computer Science and Engineering ANUGuntur522510India Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGiza12588Egypt

Graphs are used in various disciplines such as telecommunication,biological networks,as well as social *** large-scale networks,it is challenging to detect the communities by learning the distinct properties of the *** deep learning hasmade contributions in a variety of domains,we try to use deep learning techniques to mine the knowledge from large-scale graph *** this paper,we aim to provide a strategy for detecting communities using deep autoencoders and obtain generic neural attention to *** advantages of neural attention are widely seen in the field of NLP and computer vision,which has low computational complexity for large-scale *** contributions of the paper are summarized as ***,a transformer is utilized to downsample the first-order proximities of the graph into a latent space,which can result in the structural properties and eventually assist in detecting the ***,the fine-tuning task is conducted by tuning variant hyperparameters cautiously,which is applied to multiple social networks(Facebook and Twitch).Furthermore,the objective function(crossentropy)is tuned by L0 ***,the reconstructed model forms communities that present the relationship between the *** proposed robust model provides good generalization and is applicable to obtaining not only the community structures in social networks but also the node *** proposed graph-transformer shows advanced performance on the social networks with the average NMIs of 0.67±0.04,0.198±0.02,0.228±0.02,and 0.68±0.03 on Wikipedia crocodiles,Github Developers,Twitch England,and Facebook Page-Page networks,respectively.

关键词： Social networks graph transformer community detection graph classification

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Residual Attention based Long Short-Term Memory for Anomaly Human Behaviour Detection 2

Residual Attention based Long Short-Term Memory for Anomaly ...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Padma Kala, S. Aruna, M.G. Upendra Roy, B.P. Gurumoorthy, Sasikumar Alabdeli, Haider Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Dept of Cse Tamilnadu Chennai India Dayananda Sagar College of Engineering Department of Aiml Karnataka India Channabasaveshwara Institute of Technology Department of Electronics and Communication Engineering Tumkur Gubbi India Dhanalakshmi Srinivasan University Computer Science and Engineering Tamil Nadu Tiruchirappalli India The Islamic University Najaf Iraq

ISBN: (纸本)9798350382952

The recognition of video anomaly is an effective computer vision task which played an essential part in smart surveillance and safety of public however that is challenging because of their complex in real-time surveillance. Different Deep Learning (DL) algorithms utilize essential number of training information without the ability of generalization and with high complexity of time. In this research, proposed a Residual attention based Long Short-Term Memory (Residual based LSTM) method for anomaly human behaviour detection. The UMN dataset is used for detecting human behaviour and it is pre-processed by Adaptive Median Filter (AMF) and Histogram Equalization (HE). Then the pre-processed data are extracted by Residual Network 101 method which extracts essential features and then features are given to Residual based LSTM which detected the anomaly human behaviour. The evaluation parameters used for evaluating a proposed method are accuracy, precision, recall and f1-score. Proposed algorithm reached 98.71% accuracy, 98.63% precision, 98.36% recall and 98.01% f1-score which is effective than other existing algorithms like Robust technique and Spatio-Temporal descriptor. © 2024 IEEE.

关键词： Long short-term memory

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Arabic Handwritten Document OCR Solution with Binarization and Adaptive Scale Fusion Detection

arXiv

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arXiv 2024年

作者： Waly, Alhossien Tarek, Bassant Feteha, Ali Yehia, Rewan Amr, Gasser Fares, Ahmed Computer Science and Engineering Departement Faculty of Engineering Egypt-Japan University of Science & Technology E-JUST Alexandria21934 Egypt Electrical Engineering Department Faculty of Engineering Benha University Cairo11629 Egypt

The problem of converting images of text into plain text is a widely researched topic in both academia and industry. Arabic handwritten Text Recognation (AHTR) poses additional challenges due to diverse handwriting styles and limited labeled data. In this paper we present a complete OCR pipeline that starts with line segmentation using Differentiable Binarization and Adaptive Scale Fusion techniques to ensure accurate detection of text lines. Following segmentation, a CNNBiLSTM-CTC architecture is applied to recognize characters. Our system, trained on the Arabic Multi-Fonts Dataset (AMFDS), achieves a Character Recognition Rate (CRR) of 99.20% and a Word Recognition Rate (WRR) of 93.75% on single-word samples containing 7 to 10 characters, along with a CRR of 83.76% for sentences. These results demonstrate the system's strong performance in handling Arabic scripts, establishing a new benchmark for AHTR *** Codes 68T10 © 2024, CC BY-NC-SA.

关键词： Convolutional neural networks

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Advanced Lung Cancer Diagnosis and Treatment with Artificial Intelligence: Applications, Methods, and Future Directions

Advanced Lung Cancer Diagnosis and Treatment with Artificial...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Pawan Dilip Chaudhari Amit Gudadhe Artificial Intelligence and Data Science Faculty of Engineering and Technology(DMIHER) Sawangi(M) Wardha Maharashtra India Computer Science and Design Faculty of Engineering and Technology(DMIHER)Sawangi(M) Wardha Maharashtra India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

AI technology revolutionizes the treatment of lung cancer and other related lung cancer diagnoses, hence becoming the game-changing technology in the diagnosis and treatment of this serious disease. As one of the most prevalent and deadliest cancers globally, early identification, precise diagnosis, and efficient planning for the treatment of lung cancer have always been difficult. The therapy using AI-based methods is introducing another novel way of addressing the problems concerning lung cancer via advanced imaging analysis, predictive modeling, and tailored plans of treatment, the most important among which is of course, imaging analysis. What is certainly most exciting among the applications of AI in lung cancer treatment is in the realm of imaging analysis. AI has been seen to be extremely clever in the interpretation of images rather than using machine learning or deep learning but has been much faster and more accurate than a human being while doing it. Besides, these systems also increase the general accurateness of diagnosis through a reduction of the error in diagnosis and consistent results. Alluring promises are made for AI soon for the treatment of lung cancer. In this paper, we discuss how AI has been transformed into imaging, diagnosis, and treatment planning and how this will improve patient outcomes and completely transform the way healthcare is delivered. We also consider barriers still to be cleared for realizing fully the potential benefits of AI. These include problems such as the quality of available data used in model interpretation and generalizability across different healthcare settings. Therefore, we end and suggest some future research on collaboration by emphasizing the safe, effective, and ethical entry of AI into normal clinical practice.

关键词： Analytical models Renewable energy sources Ethics Lung cancer Imaging medical treatment Transforms Predictive models Planning Artificial intelligence

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Deep Learning-Based Framework for Leather Surface Defect Detection and Classification Using CNN Architectures

Deep Learning-Based Framework for Leather Surface Defect Det...

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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： R. Rajarajeswari Veeramalai Sankaradass S. Parthiban Dept of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Dept of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Science Saveetha University Chennai India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

The most critical procedure in the leather processing industry is the detection of surface defects in leather, which helps determine the amount of treated leather that is usable. Thus, identifying and locating leather flaws has a significant influence on leather hide grading. A system for identifying and classifying leather surface flaws is presented in this research. Deep learning models are used by the suggested framework to identify and categorize leather flaws. Convolutional neural networks, or CNNs, are used to identify flaws and categorize their types using the Multi Class Model (MCM) and Binary Class Model (BCM), respectively. Various CNN designs, including LeNet-5, VGG-13, VGG-16, VGG-19, ResNet-50, MobileNet, and MobileNetV2, were employed for the BCM and MCM. Out of all the models, the BCM model's test accuracy was the highest at 93.65% for the VGG-16 architecture, while the MCM model's test accuracy was 81.24% for the MobileNet architecture. The best models have been incorporated into the suggested framework once their performances are compared.

关键词： Fault diagnosis Deep learning Industries Accuracy Prototypes computer architecture Inspection Fourth Industrial Revolution Convolutional neural networks Surface treatment

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Hybrid Modeling Approaches in Energy Internet: Bridging Cyber, Physical, and Social Realms

Hybrid Modeling Approaches in Energy Internet: Bridging Cybe...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Chaturvedi, Prateek Thethi, H. Pal Reddy, N. V. Uma Kalaiarasi, M. Al-Rubaye, Taqi Mohammed Khattab Anusha, K. Amity University Greater Noida Department Of Mechanical Engineering India Lovely Professional University Phagwara India New Horizon College Of Engineering Department Of Artificial Intelligence And Machine Learning Bangalore India Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India College Of Medical Technologies The Islamic University Department Of Medical Laboratory Technology Najaf Iraq Mlr Institute Of Technology Department Of CSE-AI&ML Telangana Hyderabad India

ISBN: (纸本)9798350366846

Effective modeling approaches are required for the management and operation of the Energy Internet due to the system's complexity and mobility. New hybrid models that link the Energy Internet's digital, real-world, and social components are the primary focus of this article. We propose a novel approach that integrates adaptive reinforcement learning, social behavior analysis, and multi-agent coordination optimization to enhance the system's performance, robustness, and long-term sustainability. We intend to address issues that arise when several factors, such as evolving technology, user behavior, and social interactions, converge by employing these mixed modeling approaches. Because of this, the Energy Internet setting will run more efficiently and effectively. Through extensive simulations and case studies, we demonstrate that our proposed method outperforms conventional ones. Our main focus is on how technology can enhance energy management, better allocate resources, and back user-centered and socially inclusive policies. To ensure the seamless operation of the Energy Internet environment's cyber, physical, and social components, the results highlight the significance of mixed modeling methodologies. © 2024 IEEE.

关键词： Multi agent systems

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A Novel Methodology to Predict Flood and Landslide Disasters using Learning Assisted Artificial Intelligence (AI) Logic 3

A Novel Methodology to Predict Flood and Landslide Disasters...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Raj, G. Bhupal Patan, Muzeeb Khan Gupta, Ashwani Kumar Lakshmi, C. Gopinath, D. Yadav, Ajay Singh Sr University School of Agriculture Warangal India University of Technology and Applied Sciences College of Engineering and Technology Department of Eee Muscat Oman Chandigarh University Faculty of Business and Commerce Uttar Pradesh Unnao India Saveetha Engineering College Chennai India Department of Computer Science Karnataka Bengaluru India Srm Institute of Science and Technology Department of Mathematics Delhi-NCR Campus U.P Ghaziabad India

ISBN: (纸本)9798331509675

Comprehending the dynamics of flood and landslide prediction in current and adjacent regions is crucial for the formulation and advancement of effective predictive models, conservation, and management techniques. Conventional forecasting techniques, typically dependent on statistical models and empirical data, may prove inadequate because of the intrinsic complexities and variability of environmental elements. The complex interaction of climatic conditions, hydrological processes, geological attributes, and topographical aspects complicates precise forecasting. The constraints of traditional methods highlight the pressing necessity for more advanced and efficient predictive instruments. Artificial intelligence, especially machine learning (ML) and deep learning (DL), presents a robust paradigm that utilizes sophisticated algorithms to examine extensive datasets, discern intricate patterns, and produce more precise and prompt predictions. This proposed ViT-DL model prediction ability can greatly boost catastrophe preparedness, mitigation strategies, and ultimately, preserve lives and reduce economic damages. This study will comprehensively examine contemporary research trends, emphasizing the methodology used, model efficacy, related problems, and potential future research avenues in AI-based flood and landslide prediction. The experimental results of our proposed model produces 96.6% of Accuracy and 96.31% of Precision, comparision shows our model outperforms the overall accuracy to conventions model techniques. © 2025 IEEE.

关键词： Landslides

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Flexible Consensus Mechanism for Current Allocation in DC Microgrid

Flexible Consensus Mechanism for Current Allocation in DC Mi...

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2023 International Symposium on Electrical and Electronics engineering, ISEE 2023

作者： Tran, Khoa Dang Nguyen Ly, Trinh Thi Hoang, Khoa Truong Phuoc, Hoa Truong Le, Nhuan An Nguyen, Hung Duc Ho Chi Minh City University of Technology VNU-HCM Faculty of Electrical and Electronics Engineering Ho Chi Minh City Viet Nam University of Science Vietnam National University Faculty of Physics and Engineering Physics Department of Physics and Computer Science Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350315486

Renewable energy sources (RESs) have become the predominant trend in the development of energy systems in many countries. To coordinate RESs, microgrids have emerged as an efficient and optimal solution. However, the microgrid is facing the problem of inaccurate power sharing between the generators. To address the issue, this paper proposes a control technique that combines a dynamic consensus with droop control algorithms. The proposed method calculates current sharing error by utilizing current values and consensus algorithm. Then, an external current-sharing controller is adopted to regulate the droop parameter in an inner controller for improving the current sharing among RESs. By applying the proposed control method, the current sharing accuracy is achieved in the microgrid system under different load conditions. The proposed technique is analyzed and evaluated with the scale-down microgrid consisting of three distributed generators. The simulation results of the scale-down microgrid are obtained from the PLECS simulation platform. © 2023 IEEE.

关键词： Controllers

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