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Development of an Enhanced Convolutional Neural Network for Self-proctoring in Online Examination systems

Development of an Enhanced Convolutional Neural Network for ...

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IEEE international conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Olufemi S Ojo Mayowa O. Oyediran Busolami E. Oluwadamilare Adedayo W. Elegbede Janet O. Jooda Olufikayo A. Adedapo Department of Computer Science Ajayi Crowther University Oyo Oyo State Nigeria Department of Computer Engineering Ajayi Crowther University Oyo Oyo State Nigeria Department of Computer Engineering Bells University of Technology Ota Nigeria Department of Computer Engineering Redeemer ’s University Ede Nigeria Department of Mathematical and Computer Sciences Fountain University Osogbo

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

Self- proctoring systems leverage on artificial intelligence algorithms to detect abnormal behaviour during examination, and the Convolutional Neural Network (CNN) has been proven to be one of the most preferred deep learning algorithms to serve this purpose. Convolutional neural networks (CNN) are good for human activity recognition, detection and classification purposes, however, they require a lot of training data which can slow down execution and increase computational time during classification. Therefore, this research addressed the aforementioned problem by developing an enhanced convolutional neural network for online examination self-proctoring systems. The technique was achieved by selecting optimal weight values in the Convolutional network with a Gravitational Search Algorithm optimizer which ultimately improved the model’s computation time. The result of the CNN-GSA technique achieved an F1-Score of 91%, Accuracy of 83.33%, Precision of 83% and a recall of 99.98% at 0.657 seconds.

关键词： Training measurement Deep learning Humanities Accuracy Computational modeling Training data Classification algorithms Convolutional neural networks Human activity recognition

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Adaptive Chunking for VideoRAG Pipelines with a Newly Gathered Bilingual Educational Dataset

Adaptive Chunking for VideoRAG Pipelines with a Newly Gather...

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international CSI computer conference, CSICC

作者： Arshia Hemmat Kianoosh Vadaei Melika Shirian Mohammad Hassan Heydari Afsaneh Fatemi dept. Computer Engineering University of Isfahan Isfahan Iran

ISBN: (数字)9798331523114

ISBN: (纸本)9798331523121

This paper introduces an innovative approach to Retrieval-Augmented Generation (RAG) for video question answering (VideoQA) through the development of an adaptive chunking methodology and the creation of a bilingual educational dataset. Our proposed adaptive chunking technique, powered by CLIP embeddings and SSIM scores, identifies meaningful transitions in video content by segmenting educational videos into semantically coherent chunks. This methodology optimizes the processing of slide-based lectures, ensuring efficient integration of visual and textual modalities for downstream RAG tasks. To support this work, we gathered a bilingual dataset comprising Persian and English mid- to long-duration academic videos, curated to reflect diverse topics, teaching styles, and multilingual content. Each video is enriched with synthetic question-answer pairs designed to challenge pure large language models (LLMs) and underscore the necessity of retrieval-augmented systems. The evaluation compares our CLIP-SSIM-based chunking approach against conventional video slicing methods, demonstrating significant improvements across RAGAS metrics, including Answer Relevance, Context Relevance, and Faithfulness. Fur-thermore, our findings reveal that the multimodal image-text retrieval scenario achieves the best overall performance, emphasizing the importance of integrating complementary modalities. This research establishes a robust framework for video RAG pipelines, expanding the capabilities of multimodal AI systems for educational content analysis and retrieval 1 1 The dataset is publicly available at: https://***/datasets/uIAICIEduViQA.

关键词： measurement Visualization Large language models Pipelines Retrieval augmented generation Education Question answering (information retrieval) Multilingual Standards Context modeling

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A Thompson Sampling Approach to User-centric Selection Problems

A Thompson Sampling Approach to User-centric Selection Probl...

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international joint conference on Neural Networks (IJCNN)

作者： Yanying Huang Haoran Gu Hong Xie Mingqiang Zhou College of Computer Science Chongqing University Chongqing China School of Computer Science and Technology University of Science and Technology of China Hefei China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

User-centric selection problems arise from mobile edge computing systems, ride-sharing applications, etc. Novel variants of the multi-play multi-armed bandit model are proposed to balance the exploration vs. exploitation tradeoff of user-centric selection problems. Two significant limitations of these variants are: (1) all plays must be assigned; (2) full feedback on the resource is required. This paper relaxes these two assumptions by assigning a subset of plays and capturing bandit feedback on the resource. We show that under this relaxation of assumptions, previous algorithms have a linear regret, i.e., failing to learn the optimal decision. We design a Thompson sampling type algorithm called USP-TS for this relaxation and prove it has a sub-linear regret. To attain a better exploration vs. exploitation tradeoff, we design a variant of the USP-TS called USP-TS-AER, which is tailored for the structure of bandit feedback on the resource. Extensive experiments validate the superior performance of USP-TS against various baselines and that USP-TS-AER can reduce the regret of USP-TS by as much as 50%.

关键词： Multi-access edge computing Algorithms Computational modeling Neural networks

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A Proposed Architecture of Mobile Service Provider Based Interactive systems

A Proposed Architecture of Mobile Service Provider Based Int...

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international conference for Convergence of Technology (I2CT)

作者： Chandni Magoo Manjeet Singh Department of Computer Science and Engineering J.C. Bose University of Science and Technology Manav Rachna University Faridabad Department of Computer Science and Technology Manav Rachna University Faridabad

Chatbots are basically designed to imitate any human interaction to automate interaction and support. Numerous benefits of chatbots has led to its increased usage by different organizations to provide essential assistance to its various customers. The current interactive systems are mainly developed from two main concepts namely NLP and Machine Learning. Despite of this, there are certain challenges and limitations of current chatbots. In this paper we will review some of the current chatbots with their limitations and highlight some of the errors identified through survey analysis of different chatbots of some companies like Airtel, Vodafone and Jio etc. A survey is done to analyze the chatbots on various metrics.

关键词： Surveys measurement Interactive systems Atmospheric modeling Oral communication Machine learning Companies

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Integration of Classification and Segmentation for computer-Aided Diagnosis System of Drowning

Integration of Classification and Segmentation for Computer-...

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international joint conference on Neural Networks (IJCNN)

作者： Yuwen Zeng Xiaoyong Zhang Kei Ichiji Noriyasu Homma Tohoku University Graduate School of Medicine Tohoku University Sendai Japan Dept. of General Engineering National Institute of Technology Sendai College Sendai Japan Tohoku University Graduate School of Medicine Center for Data-Driven Science and Artificial Intelligence Tohoku University Sendai Japan Tohoku University Graduate School of Medicine Graduate School of Biomedical Engineering Tohoku University Sendai Japan

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The decline in traditional autopsy practices has led to the rise of autopsy imaging as a non-invasive alternative. However, the shortage of forensic pathologists skilled in postmortem image interpretation presents a significant challenge. Our study addresses this gap by advancing the capabilities of computer-aided diagnosis (CAD) systems in forensic diagnosis. This study builds on a previous work which developed a CAD system for drowning diagnosis and identified a critical limitation: the inconsistency between human expertise and the decision basis of the CAD system. To alleviate this issue, we present an end-to-end CAD system based on Y-Net that not only classifies post-mortem images into drowning or non-drowning but also segments regions of interest in alignment with human expertise. Experiment results showed that the model achieved an accuracy of 0.92 for classification and a mean squared error of 0.04 for segmentation, offering a promising performance and medically consistent results in drowning diagnosis.

关键词： Image segmentation Visualization Solid modeling Accuracy Forensics Autopsy Neural networks

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Smart psychological estimation and modeling based on brain-computer interface and data visualization with Python 5

Smart psychological estimation and modeling based on brain-c...

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5th international conference on Intelligent Computing and Control systems, ICICCS 2021

作者： Guo, Zhiqiang Xu, Lina School of Computer Zhuhai College of Science and Technology Zhuhai Guangdong519041 China College of Instrumentation Electrical Engineering Jilin University Changchun Jilin130061 China School of Electronic and Information Engineering Zhuhai College of Science and Technology Zhuhai Guangdong519041 China

ISBN: (纸本)9781665412728

Smart psychological estimation and modeling based on brain-computer interface and data visualization with Python is studied in this manuscript. With the continuous deepening of cognitive science and the continuous development of computer technology, cognitive modeling will surely provide operational scientific methods and approaches for exploration of the mechanism of psychological measurement analysis. When the brain is stimulated by the outside or itself, the nerve cells in it will produce relatively weak electrical impulses, and thereby converged into EEG signals. With this feature, the BCI is utilized with the data pattern analysis to construct an efficient model. The smart system is evaluated through the testing on different data sets. The experimental results on systematic implementation have proven the performance. © 2021 IEEE.

关键词： Python

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Network Intrusion Detection by Integrating BI-LSTM for Enhanced Security

Network Intrusion Detection by Integrating BI-LSTM for Enhan...

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international Symposium on Advanced Computing and Communication (ISACC)

作者： Lakshmi Priya P. Kandibanda Lohith Joel M. Johnson Jyotsna C. Aiswariya Milan K. Dept. of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

The rising interconnectivity of digital systems has brought about the challenges of network security as high risks of data theft and unauthorized access through intrusions in networks. This paper details the development of a Network Intrusion Detection System that uses deep learning techniques and is designed to detect normal as well as anomalous activities within the network, such as zero-Day vulnerabilities, as discussed has proven that this model was capable of obtaining excellent accuracy, up to 99.25%, with precision and recall metric being well achieved, owed to advanced techniques, in this case, hyperparameter tuning. This will mean the model's capability to clearly differentiate legitimate and malicious traffic. In addition, explainable AI techniques such as SHAP and LIME deliver insights into the contributions or importance of various features about a model's decision-making.

关键词： measurement Deep learning Explainable AI Digital systems Computational modeling Neural networks Network intrusion detection Network security Intelligent systems Tuning

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Volumetric Hippocampus Segmentation Using 3D U-Net Based On Transfer Learning

Volumetric Hippocampus Segmentation Using 3D U-Net Based On ...

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IEEE international conference on Computational Intelligence and Virtual Environments for measurement systems and Applications

作者： Ramadhan Sanyoto Sugiharso Widodo I Ketut Eddy Purnama Reza Fuad Rachmadi Department of Electrical Engineering Department of Computer Engineering Faculty of Intelligent Electrical and Informatics Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (数字)9798350322996

ISBN: (纸本)9798350323009

The hippocampus, a crucial component of the human brain, is involved in fundamental cognitive processes such as learning, memory, and spatial navigation. However, it is susceptible to several neuropsychiatric disorders, including epilepsy, Alzheimer’s disease, and depression. Utilizing Magnetic Resonance Imaging (MRI) techniques with efficient spatial navigation capabilities is crucial for assessing the physiological condition of the hippocampus. Labeling the hippocampus on MRI images primarily depends on manual methods, which are time-consuming and prone to errors between observers. The issue with MRI image processing lies in its demanding computational requirements and lengthy duration. Furthermore, there is a need for more three-dimensional hippocampal datasets for training deep-learning models, in which 3D labeled medical datasets are often scarce in medical imaging. This paper introduces a 3D U-Net architecture that utilizes a transfer learning model to segment the hippocampus from different pre-trained model scenarios. The results of all test scenarios indicate that the suggested model exhibits an average Dice Score, Intersection over Union (IoU) Score, and Sensitivity exceeding 0.85, 0.75, and 0.80, respectively. The proposed methodology enhances the model’s ability to generalize within a shorter timeframe, even when dealing with limited volumetric datasets. These results are achieved through transfer learning, which decreases computational complexity by utilizing pre-learned characteristics from previous tasks.

关键词： Solid modeling Three-dimensional displays Navigation Magnetic resonance imaging Computational modeling Volume measurement Transfer learning

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Analysis and Empirical Validation of Visible Light Path Loss Model for Vehicular Sensing and Communication

Analysis and Empirical Validation of Visible Light Path Loss...

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international conference on Computing, Networking and Communications (ICNC)

作者： Hisham Abuella Md Zobaer Islam Russ Messenger John O'Hara Sabit Ekin School of Electrical and Computer Engineering Oklahoma State University Stillwater OK USA Department of Engineering Technology and Industrial Distribution Texas A&M University College Station Texas USA

ISBN: (数字)9798350370997

ISBN: (纸本)9798350371000

Advancements in lighting systems and photodetectors provide opportunities to develop viable alternatives to conventional communication and sensing technologies, especially in the vehicular industry. Most of the studies that propose visible light in communication or sensing adopt the Lambertian propagation (path loss) model. This model requires knowledge and utilization of multiple parameters to calculate the path loss such as photodetector area, incidence angle, and distance between transmitter and receiver. In this study, a simplified path loss model that is mathematically more tractable is proposed for vehicular sensing and communication systems that use visible light technology. Field measurement campaigns are conducted to validate the performance and limits of the developed path loss model. The proposed model is used to fit the data collected at different ranges of incident angles and distances. Further, this model can be used for designing visible light-based communication and sensing systems to minimize the complexity of the Lambertian path loss model, particularly for cases where the incident angle between transmitter and receiver is relatively small.

关键词： Transmitters Computational modeling Receivers Propagation losses Mathematical models Photodetectors Loss measurement

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Transformer-Based Model for the Natural Language Textual Sequences to SQL-Queries Conversion

Transformer-Based Model for the Natural Language Textual Seq...

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international computer Sciences and Information Technologies (CSIT)

作者： Kateryna Yalova Andrii Zhuravel Department of Information Systems Application University of Information Technology and Management Rzeszow Poland Systems Software Department Dniprovsky State Technical University Kamianske Ukraine

ISBN: (数字)9798331542634

ISBN: (纸本)9798331542641

The concept of using natural language instead of SQL queries formed the basis of a new type of data processing called Natural Language Interface to Database (NLIDB). This paper considers a neural network (NN) approach for the NLIDB systems development. Its essence is the transformation of the input text sequence specified by the user in natural language into commands in SQL, which is inherent in text-to-SQL and sequence-to-sequence tasks. The Transformer model architecture of the NN was justified in the research. This architecture allows the NN to convert an input sequence (such as a natural language text query) into an output sequence (translated into SQL format text query). WikiSQL dataset was used for NN learning and testing. Numerous experiments were conducted in order to determine the optimal parameters of the NN model were conducted. Bilingual Evaluation Understanding (BLEU) metric was used as a metric for evaluating the quality of converting an input text sequence to SQL queries. The BLEU metric for the neural network model trained on the WikiSQL sample is 56%.

关键词： measurement Structured Query Language Accuracy Translation Computational modeling Artificial neural networks computer architecture Transformers Vectors Machine translation

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