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LSTM-QDCNN: long short-term memory and quantum dilated convolutional neural network enabled occlusion percentage prediction

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Australian Journal of Electrical and electronics engineering 2025年第1期22卷 1-14页

作者： V. Sokashe Ghorpade, Shital Arjunsing Pardeshi, Sanjay Department of Electronics and Computer Engineering Sharad Institute of Technology College of Engineering Yadrav Maharashtra Ichalkaranji India Government Residence Women’s Polytechnic Tasgaon Dist-Sangli416 312 India Shivaji University Maharashtra Kolhapur India

Occlusion detection is vital in longer video sequences of real-time scenarios, as deformable objects generally occlude themselves or be occluded by exterior objects eventually. Occlusion can affect accuracy of the entire model significantly, because optical flow may reveal random attributes in occluded as well as neighbourhood areas. Occlusion is a challenging issue in real-world visual object tracking. Several techniques learn imprecise appearance of target while it becomes occluded by another object in the scene. Occlusion is a complicated issue that causes target loss in tracking procedure. Here, long short-term memory fused quantum dilated convolutional neural network (LSTM-QDCNN) is presented for occlusion percentage prediction. Initially, video frames are extracted from considered input video. The extracted frame is pre-processed by adaptive fuzzy filtering, and object localisation is accomplished by employing sparse fuzzy c-means with local optimal oriented pattern feature. Thereafter, occlusion category detection is conducted by deep Q-network. Lastly, occlusion percentage prediction is done by utilising LSTM-QDCNN. Additionally, LSTM-QDCNN is designed by merging deep LSTM (DLSTM) with quantum dilated convolutional neural network (QDCNN). Furthermore LSTM-QDCNN achieved maximum multiple-object tracking precision of 0.892 and minimum tracking distance of 3.714. In addition, LSTM-QDCNN achieved tracking number about 18. ©, Engineers Australia.

关键词： Long short-term memory

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FAMMNPB: Design of an Enhanced Forensic Analysis Model with Multidomain Feature Extraction and Advanced Neural Architecture for Permissioned Blockchains 9

FAMMNPB: Design of an Enhanced Forensic Analysis Model with ...

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9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Mate, Ashwini Y. Khandare, Kiran S. Hassan, Mohammad Bode, Kiran Nikesh Bode, Nikesh S. Missal, Karishma K. Nagpur India Nagpur India J D College of Engineering And Technology Department of Electronics And Telecommunication Nagpur India J D College of Engineering And Technology Department of Computer Science And Engineering Nagpur India Suryodaya College of Engineering And Technology Department of Computer Science And Engineering Nagpur India J D College of Engineering And Technology Department of Information Technology Nagpur India

ISBN: (纸本)9798350394474

This work introduces a novel approach that leverages multidomain feature extraction coupled with a 1D Convolutional Neural Network (CNN) for robust anomaly detection. To address the challenge of transaction delay predictions, we employ a Bi-directional Gated Recurrent Unit (BiGRU) RNN, for its capability to capture temporal sequences with high precision. For the task of chain splitting, the Teaching-Learning-based Optimization (TLbO) technique is used to ensure the efficient creation of sidechains. Further, we integrated DeepSHAP, in tandem with 1D CNN and BiGRU, to enhance the performance of our models, fostering better trust in the results. Our proposed methodology yields notable improvements over existing techniques. As we observed enhancements in the precision of anomaly detection by 4.9%, accuracy in detecting anomalous events by 3.5%, Recall by 4.5% and AUC by 3.9%. These advancements fortify the security posture of permissioned blockchains and optimize their operational efficacy, thus marking a significant improvement in blockchain forensics. © 2024 IEEE.

关键词： Feature extraction

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Comparative Analysis of Execution of CNN-Based Sanguine Data Transmission with LSB-SS and PVD-SS

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computer Systems Science & engineering 2023年第8期46卷 1707-1721页

作者： Alaknanda S.Patil G.Sundari Arun Kumar Sivaraman Department of Electronics and Communication Sathyabama Institute of Science and TechnologyChennaiTamil Nadu600119India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India

The intact data transmission to the authentic user is becoming crucial at every moment in the current ***;is a technique for concealing the hidden message in any cover media such as image,video;and audio to increase the protection of *** resilience and imperceptibility are improved by choosing an appropriate embedding *** paper gives a novel system to immerse the secret information in different videos with different *** audio and video steganography with novel amalgamations are implemented to immerse the confidential auditory information and the authentic user’s face image.A hidden message is first included in the audio from the multimedia file;using LSB *** Stego-video is created in the second stage by merging the authorized user’s face into the frame of the video;by using PVD ***-audio is linked again with the stego-video in the third *** incorporated perspective techniques(LSB-SS and PVD-SS algorithms)with more significant data immersing capacity,good robustness and imperceptibility are proposed in this research *** spread spectrum approach is used to increase the complexity of secret data *** different video files are tested with different voice files with the results such as PSNR,SSIM,RMSE and MSE as 52.3,0.9963,0.0024 and 0.0000059,respectively.

关键词： Audio steganography data hiding information security pixel value differencing

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Facial Emotion Recognition Using Swarm Optimized Multi-Dimensional DeepNets with Losses Calculated by Cross Entropy Function

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computer Systems Science & engineering 2023年第9期46卷 3285-3301页

作者： A.N.Arun P.Maheswaravenkatesh T.Jayasankar Department of Computer Science&Engineering Sri Venkateswara Institute of Science and TechnologyTiruvallurTamilnaduIndia Department of Electronics and Communication Engineering Anna UniversityTrichyTamilnaduIndia

The human face forms a canvas wherein various non-verbal expressions are *** expressional cues and verbal communication represent the accurate perception of the actual *** many cases,a person may present an outward expression that might differ fromthe genuine emotion or the feeling that the person *** when people try to hide these emotions,the real emotions that are internally felt might reflect as facial expressions in the form of micro *** micro expressions cannot be masked and reflect the actual emotional state of a person under *** expressions are on display for a tiny time frame,making it difficult for a typical person to spot and recognize *** necessitates a place for Machine Learning,where machines can be trained to look for these micro expressions and categorize them once they are on *** study’s primary purpose is to spot and correctly classify these micro expressions,which are very difficult for a casual observer to *** research improves upon the accuracy of the recognition by using a novel learning technique that not only captures and recognizes multimodal facial micro expressions but also has features for aligning,cropping,and superimposing these feature frames to produce highly accurate and consistent results.A modified variant of the deep learning architecture of Convolutional Neural Networks combined with the swarm-based optimality technique of the Artificial Bee Colony Algorithm is proposed to effectively get an accuracy of more than 85%in identifying and classifying these micro expressions in contrast to other algorithms that have relatively less *** of the main aspects of processing these expressions from video or live feeds is aligning the frames homographically and identifying these concise bursts of micro expressions,which significantly increases the accuracy of the *** proposed swarm-based technique handles this in the research to precisely alig

关键词： Facial micro expression recognition deep learning CNN artificial bee colony

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Experimental Fire Detection and Alarm System Based on the Integration of IoT and Image Processing

Experimental Fire Detection and Alarm System Based on the In...

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2024 IEEE International Conference on Consumer electronics-Asia, ICCE-Asia 2024

作者： Vuong, Dat Tran, Van Vu Tran, Van Thanh Nhat Tran, Huynh Anh Hao Le, Pham Hoang Son, Tran The Vietnam-Korea University of Information and Communication Technology Faculty of Computer Engineering and Electronics Da Nang Viet Nam

ISBN: (纸本)9798331530839

Fires are considered a risk that causes human death and property loss. To reduce fire damage, fires must be detected and warned promptly. This paper aims to design and experimentally implement a fully-function fire detection and warning system. The developed system is based on a Wireless Sensor Network (WSN) that includes various nodes distributed across the city. Each node has two main blocks which are the image processing and communication blocks. The image processing block applies the EfficientN et B0 model to detect the fire and smoke, whereas the communication block uses the Module 4G to contact the house owner over a mobile phone network and LoRa gateway to send the signal warning to the fire center over the a LoRaWAN. The Module 4G is responsible for sending captured images of suspected fire if any to the house owner's device for getting his/her acknowledgement. Then an urgent alarm message consisting of fire position and risk level are sent to the fire center if the fire is acknowledged by the house owner, otherwise, the alarm is ceased. Besides, the proposed system is installed the gas sensor, smoke sensor, water spray system and light-sound alarm to augment the reliability of fire detection. The proposed system is experimentally demonstratedand achieved positive results: Delay time sending from the proposed fire detection system to the user is recorded as 5 seconds and from the user to the fire center is recorded as 10 seconds, within a LoRaWAN coverage of 1.5km. © 2024 IEEE.

关键词： Sensor nodes

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Circadian Effect on Luminance Contrast Sensitivity

Transactions of Japanese Society for Medical and Biological ...

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Transactions of Japanese Society for Medical and Biological engineering 2022年第6期60卷 158-163页

作者： Yoshikawa, Mayu Nagai, Midori Koida, Kowa Department of Electronics Engineering College of Engineering National Institute of Technology Tokyo College Japan Electronics-Inspired Interdisciplinary Research Institute Toyohashi University of Technology Japan Department of Computer Science and Engineering Toyohashi University of Technology Japan

All biological functions, including vision, areaffected by timeof day. It is known that theretinal response to visual stimuli changes according to circadian rhythms. However, the effects on visually guided behavior were poorly understood, and previous results have been inconsistent. The previous studies examined the effect of time on contrast sensitivity were also inconsistent, one reason for this could be the order effects, such as fatigue due to long measurement times. To overcome this issue, we applied a tablet PC based measurement system which made the measuring contrast sensitivity quick and easy. Observers participated in the experiment every morning, noon, evening, and night, and performed a task to detect a Gabor patch. The results showed that sensitivity improved from morning to night, and this trend was more pronounced in the mid-to high-frequency band. The improvement was reproduced even when measurements were taken once daily at random times. These results suggest that the improvement was not due to intraday learning or fatigue. We confirmed that our experimental system is useful for measuring the diurnal variation in contrast sensitivity, as it is quick and easy to use. © 2022, Japan Soc. of Med. electronics and Biol. engineering. All rights reserved.

关键词： Fatigue of materials

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A Hierarchical Aggregation Network with Edge Guidance for Sturdy Medical Image Segmentation 2

A Hierarchical Aggregation Network with Edge Guidance for St...

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2nd International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2024

作者： Rasheedha, A. Reethika, A. Rajeshwari, R. Remya, R. Kumar, M. Suresh Saranya, M.D. Department of Biomedical Engineering Sri Ramakrishna Engineering College Coimbatore India Department of Electronics and Communication Engineering Sri Ramakrishna Engineering College Coimbatore India Department of Computer Science and Engineering CMR Institute of Technology Bengaluru India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering Sri Ranganathar Institute of Engineering and Technology Coimbatore India Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Coimbatore India

ISBN: (纸本)9798331541217

Convolutional neural networks (CNNs) are essential for medical picture segmentation in clinical analysis and illness detection. On the other hand, noise levels and intraclass variability are considerable in medical photos, but interclass differences are negligible. It is difficult to extract strong contextual information and combine discriminative characteristics for fine-grained segmentation. Current techniques are unable to produce precise mask edges, resulting in fuzzy borders and decreased segmentation precision. In this research, a new A Hierarchical Aggregation Network with Edge Guidance that maintains precise spatial features and gathers rich contextual information is presented. The Efficient Fusion Attention Module is used by the proposed model to highlight important spatial and channel information, the Adaptive Hierarchical Feature Aggregation Module to maximize feature fusion, and the Inter-layer Edge-aware Module to improve edge prediction accuracy. Experimentally and qualitatively, the proposed method will perform better than six other cutting-edge segmentation techniques. © 2024 IEEE.

关键词： Convolutional neural networks

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Evaluation of the Explainable AI-NLP Framework for Text Categorization

Evaluation of the Explainable AI-NLP Framework for Text Cate...

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

作者： Singh, Amar Preet Gupta, Gaurav Soumya, K. Noida Institute Of Engineering And Technology Department Of Electrical & Electronics Engineering Uttar Pradesh Greater Noida India Tula's Institute Department Of Computer Science And Engineering Dehradun India Department Of Computer Science And Information Technology Bangalore India

ISBN: (纸本)9798350366846

Although there has been a decrease in interpretability, significant progress has lately been achieved in developing state-of-the-art models. This assessment provides a thorough analysis of Explainable AI (XAI) within the framework of Natural Language Processing (NLP). This study examines the fundamental classification of descriptions in addition to the many techniques for producing and displaying explanations. The purpose of the survey is to provide a comprehensive study of the many approaches and strategies used to clarify the models' predictions, making it a helpful tool for people who develop NLP models. Additionally, we draw attention to the gaps in the current literature and urge further investigation into potential avenues for this crucial field of study. An experiment is also conducted and the results are discussed in this article. © 2024 IEEE.

关键词： Natural language processing systems

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Analysis of Rain Visibility Attenuation on Free Space Optics (FSO) for South African Weather Conditions 4

Analysis of Rain Visibility Attenuation on Free Space Optics...

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Mahlobogwane, Zanele Mukubwa, Emmanuel Pillay, Nelendran Tshwane University of Technology Department of Computer Systems Engineering Pretoria South Africa Moi University KipKenya Department of Electrical and Communications Engineering Cheptiret Kenya Durban University of Technology Department of Electronics and Computer Engineering Durban South Africa

ISBN: (纸本)9798350395914

The Free Space Optics (FSO) is a high-speed connectivity system that has the same advantages of speed and bandwidth as that of optic fiber. In comparison to optic fiber, FSO is the preferred choice when it comes to cost and deployment period. However, one of the main challenges with FSO is the changing weather conditions that affect the signal since the transmitted signal is radiated through the atmosphere. This investigation focuses on examining the variables that influence the reduction of visibility at different wavelengths: 750nm, 780nm, 850nm, and 1550nm. The locations of interest concerning visibility and rain data are Pretoria, Upington, Bloemfontein, and Cape Town in the Republic of South Africa. Results indicate that fog attenuates the signal more when compared to rain. Furthermore, signals transmitted at 1550nm will attenuate the signal less when compared to the other wavelengths of interest. © 2024 IEEE.

关键词： Rain

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Smart Travel Solutions Harnessing Artificial Intelligence for Optimal Route Efficiency

Smart Travel Solutions Harnessing Artificial Intelligence fo...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Mohanapriya, D. Indumathi, R. Priyatharsini, S. Arounesh, A. Kumar, V.Kishore Kamalan, R. Manakula Vinayagar Institute of Technology Department of Computer Science and Engineering Puducherry India Karpagam Academy of Higher Education Department of Electronics and Communication Engineering Coimbatore India Manakula Vinayagar Institute of Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798331510022

This AI-based route optimizing system has put its focal point on only efficient travel routes based on real-time traffic. It focuses on calculating the shortest distance to source, along with destination and via points, distance, time-based traffic details, in order to arrive at the most optimal route for any journey. Advanced AI techniques in optimizing systems continuously adjust the route according to live traffic patterns so that users may experience the least delay possible. The system integrates with fuel consumption estimations provided the traveler through inputting vehicle mileage, enabling travelers to make better plans for their trips by considering time and fuel efficiency. This would save one time spent traveling, saves fuel, thus saving money while keeping the environment free from pollution. By such smart decisions on making routes, the system increases drivers' overall driving experience, especially in crowded areas or where there is heavy traffic. Since the system is inherently real-time, it's therefore very adaptive to the changing nature of road conditions, thus travel stream lined for the users. © 2024 IEEE.

关键词： Transportation routes

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