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Detection of Behavioral Patterns Employing a Hybrid Approach of Computational Techniques

computers, Materials & Continua 2022年第7期72卷 2015-2031页

作者： Rohit Raja Chetan Swarup Abhishek Kumar Kamred Udham Singh Teekam Singh Dinesh Gupta Neeraj Varshney Swati Jain Department of Information Technology Guru Ghasidas VishwavidyalayaBilaspur495009India Department of Basic Science College of Science&Theoretical StudiesSaudi Electronic University13316Saudi Arabia Department of Computer Science&IT JAIN(Deemed to be University)Bangalore560069India Computer Science and Information Science Cheng Kung University621301Taiwan School of Computer Science University of Petroleum and Energy StudiesDehradun248007India Department of CSE I K Gujral Punjab Technical UniversityJalandhar144603India Department of Computer Engineering and Applications GLA UniversityMathura281406India Department of Computer Science Government J Yoganandam Chhattisgarh CollegeRaipur492001India

As far as the present state is concerned in detecting the behavioral pattern of humans(subject)using morphological image processing,a considerable portion of the study has been conducted utilizing frontal vision data of human *** present research work had used a side vision of human-face data to develop a theoretical framework via a hybrid analytical model *** this example,hybridization includes an artificial neural network(ANN)with a genetic algorithm(GA).We researched the geometrical properties extracted from side-vision human-face *** additional study was conducted to determine the ideal number of geometrical characteristics to pick while *** close vicinity ofminimum distance measurements is done for these clusters,mapped for proper classification and decision process of behavioral *** identify the data acquired,support vector machines and artificial neural networks are utilized.A method known as an adaptiveunidirectional associative memory(AUTAM)was used to map one side of a human face to the other side of the same *** behavioral pattern has been detected based on two-class problem classification,and the decision process has been done using a genetic algorithm with best-fit *** developed algorithm in the present work has been tested by considering a dataset of 100 subjects and tested using standard databases like FERET,Multi-PIE,Yale Face database,RTR,CASIA,*** complexity measures have also been calculated under worst-case and best-case situations.

关键词： Adaptive-unidirectional-associative-memory technique artificial neural network genetic algorithm hybrid approach

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Single Image Super Resolution Based on Dual-Path Large Kernel Learning 1

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2nd International Conference on Internet of Things, Communication and Intelligent technology, IoTCIT 2023

作者： Jiang, He Liu, Gui Cao, Gaoting Zheng, Ping Zhang, Haoxiang Kou, Qiqi Xu, Feixiang Cheng, Deqiang Department of Information and Control China University of Mining and Technology Xuzhou China Department of Computer Science China University of Mining and Technology Xuzhou China Department of Information and Electronics Beijing Institute of Technology Beijing China

ISBN: (数字)9789819727575

ISBN: (纸本)9789819727568

To excel in image super-resolution, deep neural network-based models often employ a stacking strategy of network modules. However, this approach leads to parameter explosion and information redundancy, thereby restricting the feasibility of deploying these models on mobile devices. To tackle this challenge, this study introduces a lightweight Dual-path Large Kernel Learning approach, namely DLKL, and applies it to image super-resolution. In DLKL, the first step involves the utilization of multi-scale large kernel decomposition to effectively establish long-range dependencies between pixels and successfully preserve local information. Subsequently, DLKL enhances the feature expression ability and achieves effective feature fusion through its dual-path network architecture. Furthermore, DLKL reduces the number of parameters while maintaining performance, thus striking a balance between network performance and efficiency. The remarkable performance of DLKL has been validated through a multitude of experiments by quantitative metric tests and visual evaluations. Comparative analysis, conducted against other prevailing algorithms, provides evidence of the DLKL method’s consistent proficiency in generating images featuring enhanced texture clarity and more faithful representation of natural structures. These findings serve to reinforce the method’s inherent superiority and robustness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Network architecture

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Lip Reading with 3D Convolutional and Bidirectional LSTM Networks on the GRID Corpus 2

Lip Reading with 3D Convolutional and Bidirectional LSTM Net...

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2nd IEEE International Conference on Networks, Multimedia and information technology, NMITCON 2024

作者： Prashanth, B.S. Manoj Kumar, M.V. Puneetha, B.H. Lohith, R. Darshan Gowda, V. Chandan, V. Sneha, H.R. Nitte Meenakshi Institute of Technology Department of Information Science & Engineering Karnataka Bangalore560064 India Bapuji Institute of Engineering and Technology Department of Computer Science and Business Systems Karnataka Davanagere577004 India

ISBN: (纸本)9798350372892

In recent years, the application of artificial intelligence has revolutionized the field of lip reading by enabling the development of sophisticated models capable of accurately interpreting lip movements from video data. This work presents a novel deep learning approach to lip reading, focused on decoding spoken text from video sequences of lip movements. Traditional lip reading methods involve separate stages for visual feature design and prediction. The proposed system utilizes an endto-end deep learning model to directly map video frames to text transcriptions, leveraging 3D convolutional neural networks and bidirectional Long Short-Term Memory. By analyzing visual cues from lipmotions, the system can interpret speech, improving accessibility for individuals with hearing impairments and enabling communication in noisy environments. Compared to existing lip reading techniques, the deep learning model achieves superior performance on benchmark datasets, demonstrating its effectiveness in this challenging task. The model has achieved a character error rate of 1.54% and a word error rate of 7.96% on the best model. © 2024 IEEE.

关键词： Convolutional neural networks

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Prediction of Steel Plate Fault Classification Using CART Fuzzy Logic and ANFIS Models 9

Prediction of Steel Plate Fault Classification Using CART Fu...

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9th International Conference on information technology Trends, ITT 2023

作者： Akpinar, Mustafa Adak, M. Fatih Higher Colleges of Technology Computer and Information Science Sharjah United Arab Emirates Sakarya University Computer Engineering Department Sakarya Turkey

ISBN: (纸本)9798350327502

Some systems correct faulty production to reduce the cost and increase the performance of steel plates. The first element of quality production is to identify and classify defects. However, when the error classification is not done well, the cost of correcting the error increases. This study proposes a classification and regression tree (CART) based fuzzy model to determine which fault class the faulty steel plates fall into. The second model used in the study is the adaptive neuro-fuzzy interface system (ANFIS). In both approaches, error classes were determined using fuzzy logic. In the two models created, it was observed that when the information of the detected faulty steel plate was entered, it was unsuccessful in determining which class this fault belonged to. Although it suggests a quick solution for detecting the error class, it has been seen that these approaches are not appropriate to use because they do not offer the right solution. © 2023 IEEE.

关键词： Errors

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Active Power Curtailment and Reactive Power Control in PV-Rich Low-Voltage Distribution Network 20

Active Power Curtailment and Reactive Power Control in PV-Ri...

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20th International Conference on the European Energy Market, EEM 2024

作者： Dubravac, Marina Topić, Danijel Fekete, Krešimir Šimić, Zvonimir Prenc, Rene Rojnić, Michele Josip Juraj Strossmayer University of Osijek Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia University of Rijeka Faculty of Engineering Rijeka Croatia

ISBN: (纸本)9798350381740

High PV systems integration faces distribution networks (DN) with technical issues. However, PVs are inverter- based sources and with active power curtailment (APC) and reactive power control (RPC) can provide ancillary services for distribution system operators (DSOs) and participate as problem/solution principle. This paper proposes an optimal power flow (OPF)-based model for improving voltages and reducing losses by using PV inverter APC and RPC strategies. The proposed optimization model is presented as nonlinear programming (NLP). For solving this optimization problem without approximations, a co-simulation of OpenDSS power system analysis software and PyGMO package with embedded computational intelligence (CI) optimization methods is used. Several case studies are carried out over the IEEE European low-voltage test feeder (LVTF). Regardless of which strategy is applied, the obtained results show that the proposed model improves technical conditions in PV-rich DN and illustrate PV potential in providing ancillary services to DSO. © 2024 IEEE.

关键词： Power distribution networks

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Data Hiding in AMBTC Images Using Selective XOR Hiding Scheme

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computers, Materials & Continua 2022年第6期71卷 5167-5182页

作者： Yung-Yao Chen Yu-Chen Hu Ting-Kai Yang You-An Wang Department Electronic and Computer Engineering National Taiwan U.Science and TechnologyTaipei106Taiwan Department Computer Science and Information Management Providence UniversityTaichung433Taiwan Grad.Inst.Automation Technology National Taipei University of TechnologyTaipei106Taiwan

Nowadays since the Internet is ubiquitous,the frequency of data transfer through the public network is *** secure data in these transmitted data has emerged broad security issue,such as authentication and copyright *** the other hand,considering the transmission efficiency issue,image transmission usually involves image compression in Internet-based *** address both issues,this paper presents a data hiding scheme for the image compression method called absolute moment block truncation coding(AMBTC).First,an image is divided into nonoverlapping blocks through AMBTC compression,the blocks are classified four types,namely smooth,semi-smooth,semi-complex,and *** secret data are embedded into the smooth blocks by using a simple replacement *** proposed method respectively embeds nine bits(and five bits)of secret data into the bitmap of the semi-smooth blocks(and semicomplex blocks)through the exclusive-or(XOR)*** secret data are embedded into the complex blocks by using a hidden *** the embedding phase,the direct binary search(DBS)method is performed to improve the image qualitywithout damaging the secret *** experimental results demonstrate that the proposed method yields higher quality and hiding capacity than other reference methods.

关键词： Content protection technology security for image data absolute moment block truncation coding(AMBTC) direct binary search(DBS)

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New Optimization Method Based on Binary Tumbleweed Algorithm 1

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18th International Conference on Intelligent information Hiding and Multimedia Signal Processing, IIH-MSP 2022

作者： Pan, Jeng-Shyang Wang, Gui-Ling Yang, Dongqiang Chu, Shu-Chuan College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China School of Computer Science and Technology Shandong Jianzhu University Jinan China Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan

ISBN: (数字)9789819906055

ISBN: (纸本)9789819906048

Tumbleweed algorithm (TA) is a meta-heuristic algorithm that simulates the growth of Tumbleweed. Compared with other advanced meta-heuristic algorithms, TA has strong optimization performance and competitiveness. However, TA is a continuous optimization algorithm, which is not suitable for binary problems. This paper presents a binary Tumbleweed optimization algorithm (BTA). The continuous search space is mapped into the discrete space by V-shaped and S-shaped transfer functions. In addition, this paper uses the CEC2013 benchmark function to evaluate the efficiency of BTA. To prove the effectiveness of the proposed algorithm, the BTA is compared with four advanced binary optimization algorithms. The experimental results reflect that BTA has preferable optimization capabilities on unimodal, multimodal, and composite functions. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Heuristic algorithms

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PNRITR:Research on Positive and Negative Entity Region Perception Methods for Image-Text Retrieval 36

PNRITR:Research on Positive and Negative Entity Region Perce...

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36th International Conference on Software Engineering and Knowledge Engineering, SEKE 2024

作者： Zhang, Zhiping Sun, Tao Zheng, Hongyan Liu, Hao Liu, Gengchen Yang, Zhi Wang, Xiaoyu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)1891706594

The aim of cross-modal image-text retrieval is to heighten comprehension and to create robust associations between visual and textual content. This process entails a mutual querying and synchronization across various data formats. While conventional techniques have seen enhancements via attention-based approaches, they frequently miss the dynamics between corresponding and non-corresponding entity areas within image-text pairs. These techniques, often fixated on aligning image entities with textual terms, can overlook the congruence of entity characteristics, leading to less accurate localization. This research introduces PNRITR, an innovative strategy for cross-modal retrieval that leverages label learning to discern attributes absent from images, broadening the scope beyond just positive entity areas. PNRITR has delivered notable advancements on the Flickr30K and MS-COCO datasets. © 2024 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Knowledge based systems

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Incorporating 5G NR Sidelink with MQTT to Maximize Energy Efficiency in IoT-Enabled Microgrids 1

Incorporating 5G NR Sidelink with MQTT to Maximize Energy Ef...

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1st IEEE International Conference on Smart Power Control and Renewable Energy, ICSPCRE 2024

作者： Neelamegam, G. Ashforn, Hermina J.M. Raja, Mohamed S. Priyadharshini, J. Akshya, J. Choudhry, Mani Deepak Sundarrajan, M. Vsb College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Erode India Kalaignarkarunanidhi Institute of Technology Department of Computer Science and Engineering Coimbatore India K. Ramakrishnan College of Engineering Department of Information Technology Trichy India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India Srm Institute of Science and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798350377002

IoT for smart microgrids is an innovative disruption at the cutting edge of energy management, relying on interconnectedness provided by smart sensors and devices to enhance power generation and distribution precision in metropolitan areas. The problems of the real-time operational capabilities required for efficient energy resource management thus progress, inhibit rapid control and management. The main goal of this research proposal consists of to use of the advanced technology of 5G NR Sidelink combined with MQTT to conduct a revolutionary communication system of Microgrid running through the IoT devices. The 5G NR (New Radio) Sidelink enables direct D2D communication, which leads to a dramatic lowering of the latency in the absence of traditional network infrastructures for communication, while the MQTT protocol improves the communication efficiency among networks of numerous IoT devices. The introduction of this system was followed by a 15% decrease in energy transmission losses and by 20% improvement in response times in comparison to conventional models specified in the quantitative evaluation thus increasing operational efficiency by far. These efficiencies not only reduce operating expenses and power consumption but also increase the solution's scalability, pointing out the possibility of building the solution for bigger urban microgrid implementations. In consideration of that, the suggested mechanism with 5G NR Sidelink along with MQTT overcomes elaborate barriers of IoT-based microgrids and establishes a new potency in advanced energy management in smart cities which could make the distribution network in the urban areas on the edge of the verge of a change. © 2024 IEEE.

关键词： Microgrids

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The Effectiveness of CNN-Based Models in Macroscopic Motion Direction Detection 6

The Effectiveness of CNN-Based Models in Macroscopic Motion ...

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6th International Conference on Robotics and computer Vision, ICRCV 2024

作者： Qiu, Zhiyu Chen, Tianqi Hua, Yuxiao Todo, Yuki Graduate School of Natural Science & Technology Kanazawa University Division of Electrical Engineering and Computer Science Kanazawa Japan Kanazawa University Faculty of Electrical Information and Communication Engineering Kanazawa Japan

ISBN: (纸本)9798331527426

Convolutional Neural Networks (CNNs) have revolutionized the field of computer vision, offering significant advancements in image and video recognition, classification, and segmentation tasks. Leveraging hierarchical pattern recognition capabilities, CNNs have demonstrated superior performance over traditional machine learning methods. One particularly critical application of CNNs is in motion direction detection, which is essential for enhancing security in surveillance systems, ensuring safe navigation in autonomous vehicles, and providing valuable insights in sports analytics. In this study, our primary goal was to investigate how different types of input data impact the accuracy of motion direction detection. We applied five distinct preprocessing methods to test the effect of input data on accuracy. Furthermore, we conducted experiments in various object and background scenarios to evaluate adaptability of simple 4-layer CNN, EfficientNetBO and ResNet50 in detecting motion direction. Finally, we tested the noise resistance of these three models. The results indicate that CNNs are capable of extracting the macroscopic motion direction of objects in images. Our study highlights the potential of CNN-based models for efficient and effective motion direction detection. © 2024 IEEE.

关键词： Convolutional neural networks

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