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Deception Detection Deep Learning Comprehensive system Utilizing Explainable AI 16

Deception Detection Deep Learning Comprehensive system Utili...

引用

16th International Conference on Developments in eSystems engineering, DeSE 2023

作者： Salah, Suhaib Khater, Tarek Almajali, Eqab Khan, Wasiq Hussain, Abir University of Sharjah Dept. of Electrical Engineering Sharjah United Arab Emirates Liverpool John Moores University Department of Computer Science Liverpool United Kingdom

ISBN: (纸本)9798350381344

Deception detection plays a vital role in various domains, from security and law enforcement to human behavior analysis. In this paper, we propose a comprehensive system for deception detection that leverages S&A smart sensing device, deep transfer learning, deep learning techniques, and explainable artificial intelligence. Our approach combines visual, auditory, thermal, cardiovascular, and respiratory cues, offering enhanced accuracy and resistance to countermeasures. Deep Transfer Learning is employed to adapt pre-trained models to the deception detection task, overcoming data limitations. Incorporating Explainable AI techniques enhances transparency and interpretability, fostering trust and collaboration in human-machine interactions. Our research lays the groundwork for future advancements in deception detection technology, addressing challenges and providing promising opportunities in the realm of deception detection. © 2023 IEEE.

关键词： Deep learning

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Handwritten Character Recognition of Telugu Characters 8

Handwritten Character Recognition of Telugu Characters

引用

8th IEEE International Conference for Convergence in Technology, I2CT 2023

作者： Wasalwar, Yash Prashant Singh Bagga, Kishan Bhogendra Rao, P.V.R.R. Dongre, Snehlata G H Raisoni College of Engineering Dept. of Computer Science and Engineerng Nagpur India DRDO Kanchanbagh Defence Research and Development Laboratory Hyderabad India

ISBN: (纸本)9798350334012

Given the cursive structure of the writing and the similarity in shape of the letters, Telugu handwritten character identification is an interesting topic. The lack of Telugu-related handwritten datasets has slowed the development of handwritten word recognizers and forced researchers to compare various approaches. Modern deep neural networks find it difficult because they often need hundreds or thousands of photos per class. It has been demonstrated that learning important aspects of machine learning systems can be computationally expensive and challenging when there is a limited amount of data available. This research analysis work proposes a use case on the pre-existing model called EfficientNet and on top of that a custom pooling layer is added to check the trend as the dataset size increases of Telugu characters. The dataset has been divided into three categories, namely, Vowels only dataset, Consonant only dataset, and All character dataset. Proposed model was trained with a considerable amount of dataset containing half a thousand of handwritten Telugu characters and has produced some fascinating results which were worth observing. The accuracies had followed a certain trend. The model was tested on the dataset collected, which were filtered out to record any performance improvement and improvement was observed, where average accuracy went from 55% to 92%. © 2023 IEEE.

关键词： Character recognition

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Involutional Neural Network Based Approach for the Detection of Melanoma Cancer

Involutional Neural Network Based Approach for the Detection...

引用

computer, Electronics & Electrical engineering & their Applications (IC2E3), International Conference on

作者： Deepak Upadhyay Kunj Bihari Sharma Mridul Gupta Abhay Upadhyay Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era deemed to be University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India

ISBN: (数字)9798350388534

ISBN: (纸本)9798350388541

This paper presents an extraordinary method to detected melanoma, the most lethal form of skin cancer, using Involutional Neural Networks (INNs). Melanoma's early detection is crucial for effecacius treatment, yet existing diagnostic methods often lack the requiring precision and scalability. INNs offer a solve through their advanced capability to adaptively process spatial information in images, a huge different from traditional convolutional techniques. Our reaserch applies INNs to melanoma detectionair, focusing on their unique involution operations that significant enhance image analysis for accurately classification. The performance of our model, assessed across different metrics including accurate, precision, recall, and F1-scores, demonstrates its superior diagnostic capability. This investigation not only underscores the potency of INNs in medical imagining but also opens up new avenues for their application across diverse analytical needs in healthcare. By advancing melanoma detection methods, we contribute to the broader goal of improving early cancer diagnose and treatment outcomes!

关键词： Measurement Training Accuracy Scalability Neural networks Melanoma Predictive models Stability analysis Reliability Thermal stability

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3D CNN Implementation in Kinesis Recognition & Sign Language

3D CNN Implementation in Kinesis Recognition & Sign Language

引用

2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Chaudhary, Mayank Mahajan, Aryan Raj, Gaurav Sar, Ayan Choudhury, Tanupriya Pant, Bhasker Dewangan, Bhupesh Kumar Sharda University Dept. of Computer Science Greater Noida201310 India School of Computer Science Uttarakhand Dehradun248007 India Graphic Era Deemed to Be University Uttarakhand Dehradun248002 India Graphic Era Deemed to Be University Cse Dept Uttarakhand Dehradun248002 India Op Jindal University Department of Computer Science and Engineering Chhattisgarh Raigarh India

ISBN: (纸本)9798350373783

The 3D Convolutional Neural Network (CNN) is used for Kinesis Recognition in this paper. The suggested model exhibits enhanced recognition accuracy of intricate hand and body movements by acquiring spatiotemporal information from kinesis sequences. The network can more reliably recognize complex motions because it can simultaneously utilize spatial and temporal information. By means of comprehensive testing and assessment, this work demonstrates the efficacy of the 3D CNN architecture in real-time kinesis categorization, indicating that it is a viable option for a variety of uses, including virtual reality, sign language interpretation, and human-computer interaction. © 2024 IEEE.

关键词： Convolutional neural networks

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An Ensemble Deep Learning Framework for Binary Classification of COVID-19 Cases Using the VGG16 (Visual Geometry Group 16) Architecture: A Comprehensive Analysis and Performance Evaluation

An Ensemble Deep Learning Framework for Binary Classificatio...

引用

Disruptive Technologies (ICDT), International Conference on

作者： Deepak Upadhyay Mridul Gupta Somesh Mishra Saksham Mittal Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

This paper presents a novel approach to deep learning by putting forth a cooperative system that uses the VGG16 architecture to categorise COVID-19 examples into two groups. Our model is distinguished by its remarkable recall metrics and precision, which achieve a careful balance that is essential for accurate categorization. What's more impressive is how well the model performs in the non-COVID category, effectively differentiating between COVID and non-COVID cases. With a remarkable overall accuracy of 96%, the model successfully classifies cases from both groups, demonstrating the potential of our suggested framework as a useful diagnostic tool useful in various clinical contexts. This work clarifies the effectiveness of deep learning techniques, concentrating on the VGG16 architecture in the crucial job of binary classification for COVID-19 identification. Our results open up new avenues for investigation in the field of accurate medical diagnosis in addition to providing insights into the real-world applications of sophisticated machine learning. The study highlights the ensemble approach's encouraging benefits, showing how it may strengthen diagnostic precision and advance clinical decision-making.

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Implementation of Pruned and Quantized Semantic Segmentation Neural Network Using Cambridge-Driving Labeled Video Database (CamVid) Dataset

Implementation of Pruned and Quantized Semantic Segmentation...

引用

Device Intelligence, Computing and Communication Technologies, (DICCT), International Conference on

作者： Deepak Upadhyay Sonal Malhotra Mridul Gupta Somesh Mishra Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798350372847

ISBN: (纸本)9798350372854

In This research investigates the streamlined implementation of Semantic Segmentation Neural Networks through advanced techniques: pruning and quantization. Leveraging the CamVid dataset, our study achieved remarkable reductions in computational complexity and memory usage. Pruning removed redundant connections, effectively reducing learnable parameters, while quantization significantly minimized memory footprint. Despite these optimizations, the networks maintained high semantic segmentation accuracy. The implications of our findings are pivotal, particularly in resource-constrained applications like autonomous driving and image analysis. By enhancing efficiency without compromising accuracy, this research facilitates the seamless integration of deep learning models into real-time systems. Our study not only advances the field of computer vision but also underscores the practical feasibility of deploying sophisticated neural networks in practical, resource-efficient contexts.

关键词： computer vision Quantization (signal) Image analysis Semantic segmentation Computational modeling Artificial neural networks Real-time systems

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Multi-Point Positional Insertion Tuning for Small Object Detection

Multi-Point Positional Insertion Tuning for Small Object Det...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Kanoko Goto Takumi Karasawa Takumi Hirose Rei Kawakami Nakamasa Inoue Dept. of Computer Science Institute of Science Tokyo Tokyo Japan Dept. of Systems and Control Engineering Institute of Science Tokyo Tokyo Japan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Small object detection aims to localize and classify small objects within images. With recent advances in large-scale vision-language pretraining, finetuning pretrained object detection models has emerged as a promising approach. However, finetuning large models is computationally and memory expensive. To address this issue, this paper introduces multi-point positional insertion (MPI) tuning, a parameter-efficient finetuning (PEFT) method for small object detection. Specifically, MPI incorporates multiple positional embeddings into a frozen pretrained model, enabling the efficient detection of small objects by providing precise positional information to latent features. Through experiments, we demonstrated the effectiveness of the proposed method on the SODA-D dataset. MPI performed comparably to conventional PEFT methods, including CoOp and VPT, while significantly reducing the number of parameters that need to be tuned.

关键词： Computational modeling Object detection Signal processing Feature extraction Encoding Acoustics Speech processing Tuning

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Gene Essentiality Mediated Base Substitution in Escherichia coli genome: Machine Learning Analysis

Gene Essentiality Mediated Base Substitution in Escherichia ...

引用

2022 International Interdisciplinary Conference on Mathematics, engineering and science, MESIICON 2022

作者： Kurmi, Annushree Das, Debashis Sen, Piyali Ray, Suvendra Kumar Satapathy, Siddhartha Sankar Tezpur University Dept. of Computer Science and Engineering Tezpur India Tezpur University Dept. of Molecular Biology and Biotechnology Tezpur India

ISBN: (纸本)9781665470728

Several selection and mutation factors are known to influence evolution in the nucleotide sequence in the genome of organisms. Using the machine learning approach, we considered gene essentiality as a selection factor, and we investigated base substitution among essential and remaining gene sets in the Escherichia coli genome. RF and SVM classifiers achieved an AUC score of 75% each. The importance of the base substitution-based features differs in predicting essentiality. Features such as G to A substitutions, mutations within the amino group, and transversion mutations were found to be the most important features. In contrast, G to C, G to T, and T to C substitutions were among the least ones. This result indicates that the base substitution patterns in essential genes might differ from that in non-essential genes. The result of this manuscript will be helpful for understanding evolution at the molecular level. © 2022 IEEE.

关键词： Genes

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Smart Traffic Management System for Urban Mobility Enhancement Using RF Sensors, Cameras, and Machine Learning

Smart Traffic Management System for Urban Mobility Enhanceme...

引用

International Conference on Power, Energy, Control and Transmission Systems (ICPECTS)

作者： Vivekanandan G Pathmanaban J Prabahar Godwin James Barani Sree S Priyanka V Samvardhini V M Dept. of Computer Science and Engineering Sri Sai Ram Institute of Technology

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508852

This paper introduces a Smart Traffic Management System (STMS)employing RF sensors, cameras, and machine learning algorithms to monitor and optimize urban traffic. The system dynamically adjusts traffic signal timings, offers real-time route recommendations based on GPS data, and incorporates adaptive control mechanisms to reduce congestion and improve overall mobility. Simulation studies and real-world testing demonstrate the effectiveness of the STMS in enhancing traffic flow, minimizing waittimes, and contributing to sustainable urbandevelopment.

关键词： Radio frequency Machine learning algorithms Object detection Light emitting diodes Sensor systems Real-time systems Liquid crystal displays Object recognition Intelligent sensors Monitoring

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User-Resource Associations for Delay Minimization in THz-Enabled Mobile Edge Computing Systems 15

User-Resource Associations for Delay Minimization in THz-Ena...

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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Song, Heekang Choi, Wan School of Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Institute of New Media and Communications Seoul National University Seoul Korea Republic of Seoul National University Dept. of Electrical and Computer Engineering Seoul Korea Republic of

ISBN: (纸本)9798350364637

Mobile edge computing (MEC) and terahertz (THz)-enabled communication systems are gaining significant attention for their potential to reduce user service delays in future mobile networks. This paper leverages user offloading and resource management in MEC systems operating at THz frequencies to minimize expected user service delays. Numerical results demon-strate that our approach outperforms the baseline algorithm. © 2024 IEEE.

关键词： Mobile edge computing

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