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Multimodal Autonomous Verbal Assessment with Visual Inspection 2

Multimodal Autonomous Verbal Assessment with Visual Inspecti...

2nd International Conference on Paradigm Shifts in Communications Embedded Systems, Machine Learning and Signal Processing, PCEMS 2023

作者： Agrawal, Meet Kathale, Atharva Purohit, Sahil Sainis, Kalyani Kumar, Praveen Radke, Mansi Visvesvaraya National Institute of Technology Computer Science and Engineering Nagpur India

ISBN: (纸本)9798350310719

This paper proposes an autonomously assessing system for the verbal examination of candidates. The system uses audio-video inputs and processes them to detect the candidate's spoken answer, and compares it to the model answer in the dataset with the corresponding question. The semantic similarity score will be calculated and used to recommend the next question from the database using various types of recommendation systems discussed in the paper. Additionally, the system employs video analysis techniques to detect and prevent modern malpractices like multiple faces and reading from notes during the examination process. The proposed system aims to improve the efficiency and fairness of verbal examinations by eliminating human bias and accurately evaluating the candidate's understanding of the subject. The system performance will be evaluated using a dataset of spoken answers and the results will demonstrate its effectiveness in improving the efficiency and fairness of the verbal examination process. © 2023 IEEE.

关键词： Semantics

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Developing Lexicons for Enhanced Sentiment Analysis in Software engineering:An Innovative Multilingual Approach for Social Media Reviews

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computers, Materials & Continua 2024年第5期79卷 2771-2793页

作者： Zohaib Ahmad Khan Yuanqing Xia Ahmed Khan Muhammad Sadiq Mahmood Alam Fuad AAwwad Emad A.A.Ismail School of Automation Beijing Institute of TechnologyBeijing100081China Department of Computer Science and Technology University of Science and Technology BannuKPKBannu28100Pakistan School of Computer Science and Engineering Central South UniversityChangsha410083China Department of Quantitative Analysis College of Business AdministrationKing Saud UniversityP.O.Box 71115Riyadh11587Saudi Arabia

Sentiment analysis is becoming increasingly important in today’s digital age, with social media being a significantsource of user-generated content. The development of sentiment lexicons that can support languages other thanEnglish is a challenging task, especially for analyzing sentiment analysis in social media reviews. Most existingsentiment analysis systems focus on English, leaving a significant research gap in other languages due to limitedresources and tools. This research aims to address this gap by building a sentiment lexicon for local languages,which is then used with a machine learning algorithm for efficient sentiment analysis. In the first step, a lexiconis developed that includes five languages: Urdu, Roman Urdu, Pashto, Roman Pashto, and English. The sentimentscores from SentiWordNet are associated with each word in the lexicon to produce an effective sentiment score. Inthe second step, a naive Bayesian algorithm is applied to the developed lexicon for efficient sentiment analysis ofRoman Pashto. Both the sentiment lexicon and sentiment analysis steps were evaluated using information retrievalmetrics, with an accuracy score of 0.89 for the sentiment lexicon and 0.83 for the sentiment analysis. The resultsshowcase the potential for improving software engineering tasks related to user feedback analysis and productdevelopment.

关键词： Emotional assessment regional dialects SentiWordNet naive bayesian technique lexicons software engineering user feedback

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DeepPose: An Integrated Deep Learning Model for Posture Detection Using Image and Skeletal Data 14

DeepPose: An Integrated Deep Learning Model for Posture Dete...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Singh, Manvendra Alam Ansari, Md. Sarfaraj Chandra Govil, Mahesh National Institute of Technology Sikkim Computer Science and Engineering Sikkim India

ISBN: (纸本)9798350335095

Identifying human actions and postures presents significant challenges for computerized systems. The categorization of these tasks holds particular relevance in the fields of health and robotics. Leveraging artificial intelligence technologies, it becomes feasible to define and classify recurring physical movements and postures accurately. Proper posture is further essential for the rehabilitation of patients because it affects the effectiveness of exercise training. Unfortunately, patients fail to follow the correct sequence when performing the exercises. To pursue the problem, a new method is proposed for posture recognition and pose estimation that does not require wearable devices. The proposed model utilizes 2D coordinates derived from the 2D poses as inputs with 18 joints of the human body as key points, along with an image dataset, to accurately classify various postures. This study involved training a custom CNN named DeepPose using both image and keypoint datasets and conducting a comparative analysis with the performance of two other pre-trained models. The result shows that the proposed model with the keypoint dataset outperforms over image dataset. © 2023 IEEE.

关键词： Patient rehabilitation

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Detection of COVID-19 Using CNN and ML Algorithms

Detection of COVID-19 Using CNN and ML Algorithms

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International Conference on Data Analytics, Intelligent Computing, and Cyber Security, ICDIC 2020

作者： Raghav Srinivaas, M. Shah, Khanjan Abhishek, B. Jagadeesh Kannan, R. Balasundaram, A. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9789811941610

As we see coronavirus is the very dangerous diseases and to identify this diseases in one’s body is also not as easy. So during identification of diseases there are many false positive cases we see that person does not have corona and still the prediction comes true and also in some cases, it happens that person has corona but it does not get detected (false negative case). So due to this problem, we here come up with the two approaches and make comparison between these two approaches and decide which one is better to analyze the diseases in the body. We are using CNN to scan chest X-ray dataset and ML algorithms for tabular dataset as it contains many text information too. So in this project, we explain in detail, what is CNN, what is ML, how to implement CNN and ML algorithms on particular dataset, what output we will get as a comparison. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： CNN Corona ML

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Real Numbers Equally Compressible in Every Base 40

Real Numbers Equally Compressible in Every Base

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40th International Symposium on Theoretical Aspects of computer science, STACS 2023

作者： Nandakumar, Satyadev Pulari, Subin Department of Computer Science and Engineering Indian Institute of Technology Kanpur India

ISBN: (纸本)9783959772662

This work solves an open question in finite-state compressibility posed by Lutz and Mayordomo [20] about compressibility of real numbers in different bases. Finite-state compressibility, or equivalently, finite-state dimension, quantifies the asymptotic lower density of information in an infinite sequence. Absolutely normal numbers, being finite-state incompressible in every base of expansion, are precisely those numbers which have finite-state dimension equal to 1 in every base. At the other extreme, for example, every rational number has finite-state dimension equal to 0 in every base. Generalizing this, Lutz and Mayordomo in [20] (see also Lutz [19]) posed the question: are there numbers which have absolute positive finite-state dimension strictly between 0 and 1 - equivalently, is there a real number ξ and a compressibility ratio s ∈ (0, 1) such that for every base b, the compressibility ratio of the base-b expansion of ξ is precisely s? It is conceivable that there is no such number. Indeed, some works explore "zero-one" laws for other feasible dimensions [11] - i.e. sequences with certain properties either have feasible dimension 0 or 1, taking no value strictly in between. However, we answer the question of Lutz and Mayordomo affirmatively by proving a more general result. We show that given any sequence of rational numbers 〈qb〉∞b=2, we can explicitly construct a single number ξ such that for any base b, the finite-state dimension/compression ratio of ξ in base-b is qb. As a special case, this result implies the existence of absolutely dimensioned numbers for any given rational dimension between 0 and 1, as posed by Lutz and Mayordomo. In our construction, we combine ideas from Wolfgang Schmidt’s construction of absolutely normal numbers from [23], results regarding low discrepancy sequences and several new estimates related to exponential sums. © Satyadev Nandakumar and Subin Pulari.

关键词： Compressibility

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Automated Underwater Fish Species Recognition using Deep Learning-based Techniques 11th

Automated Underwater Fish Species Recognition using Deep Lea...

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11th International Conference on Intelligent Computing and Applications, ICRTC 2023

作者： Anantha Prabha, P. Sachin, S. Srinithish, U. Deva Priya, M. Karthick, S. Department of Computer Science and Engineering Sri Krishna College of Technology Tamil Nadu Coimbatore India Department of Computer Science and Engineering Sri Eshwar College of Engineering Tamil Nadu Coimbatore India Department of Computer Science and Engineering SRM Institute of Science and Technology Delhi-NCR Campus Uttar Pradesh Ghaziabad India

ISBN: (纸本)9789819717231

Knowledge about fish species with continuous monitoring play a dominant role in determining short and long-term effects on ecosystems and generating ways to manage the problem through specific treatment. Categorization and identification of fish species from underwater film are critical for maintaining resources of fisheries and environmental balance. It is difficult to get an accurate result from actual underwater films owing to poor resolution and object motion. Deep Learning (DL) technology is effectively used in a variety of sectors including aquaculture. In order to identify and recognize fishes in the presence of marine background noise, this research offers architecture for fish detection system that includes an updated training model and a multi-scale fish detection module using enhanced Convolutional Neural Network (CNN). The training technique entails gradual training of enhanced CNN by initially focusing on learning tough species followed by gradual learning of new species by incrementally employing knowledge distillation loss while maintaining high performance of previously learned species. This detection model employs a DL strategy based on CNN to improve detection performance on small-sized objects. As a consequence, the issues prevalent in identifying objects underwater are reduced, and performance of the framework is improved. In contrast to current identification methods, the proposed techniques offer better accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image classification

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Collaborative Problem Solving Platform with Solution Recommendation 14

Collaborative Problem Solving Platform with Solution Recomme...

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Kunthavai, A. Nivendra, K. Santhosh, M. Naveenkumar, M. Sadin, Milli Department of Computer Science and Engineering Coimbatore Institute of Technology Coimbatore India

The traditional method of problem solving involves the problem and a practise area, but there is no room for one-on-one communication in which one can teach or learn from another person. This is no longer sufficient because one-on-one communication has become the standard while solving problems nowadays. And while solving each problem, the user must search for the optimal solution if they don’t have the problem solution, thus finding the solution for the problem takes a considerable amount of time. If one person wants to teach the other, they both must be on a video/audio call in order for them to communicate and work together to understand the problem. They also need to share the screen so that both can able to view the code and can able to debug it. This entails a lot of frantic work. In this project, we propose a solution using a webapp for problem solving practise that includes features such as code collaboration, peer-to-peer audio and video interaction, allowing two or more people to work on the same problem while the editor is shared among all the connected members. Interviewers can also use this collaborative coding platform to conduct a coding interview with a candidate for a problem-solving round. © Grenze Scientific Society, 2023.

关键词： audio/video interaction One-on-.communication problem-solving web app

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Certain Investigation on Eeg Signal Processing Using Auto Regression Feature for Various Colour Stimuli

Certain Investigation on Eeg Signal Processing Using Auto Re...

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2023 International Conference on Intelligent Systems for Communication, IoT and Security, ICISCoIS 2023

作者： Saranya, K. Paulraj, M. Sri Ramakrishna Institute of Technology Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350335835

Classifying the EEG signals based on color perception is one of the interesting research areas in developing a brain machine interface. Controlling the devices based on visually evoked potential using color as stimulus is much more effective in terms of time rather than other type of potentials evoked by brain. In this paper the EEG signal is collected for nine subjects and preprocessed using a band pass filter of © 2023 IEEE.

关键词： Electroencephalography

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Hybrid cloud storage system with enhanced multilayer cryptosystem for secure deduplication in cloud

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International Journal of Intelligent Networks 2023年第1期4卷 301-309页

作者： Mageshkumar, Nagappan Swapna, J. Pandiaraj, A. Rajakumar, R. Krichen, Moez Ravi, Vinayakumar Department of Computer Science and Technology Madanapalle Institute of Technology and Science Madanapalle India Department of Computer Science & Engineering Veltech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Computing Technology SRM Institute of Science and Technology Kattankulathur chennai India Al-Baha University Alaqiq 65779-7738 Saudi Arabia ReDCAD Laboratory University of Sfax Sfax3038 Tunisia Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar34754 Saudi Arabia Centre for Automation School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Chennai India

Data deduplication is a crucial technique in the field of data compression that aims to eliminate redundant copies of recurring data. This technique has gained significant popularity in the realm of cloud storage due to its ability to effectively reduce storage requirements and optimize bandwidth utilization. To ensure the safeguarding of sensitive data while simultaneously facilitating deduplication, researchers have put forth the concept of convergent encryption as a potential solution. This technique involves encrypting the data prior to its outsourcing, thereby enhancing the confidentiality of the information. In this work, an earnest endeavor is undertaken to formally tackle the issue of authorized data deduplication, with the aim of enhancing data security. Our approach combines the Diffie-Hellman algorithm and symmetrical external decision to protect and popularize information, ensuring end-to-end encryption to encourage user adoption of cloud storage. The proposed model employs block-level deduplication and guarantees the randomness of ciphertexts by generating encryption keys using the Diffie-Hellman algorithm. This method effectively counters both internal and external brute-force attacks, enhancing data security while reducing computational costs. An extensive experimentation is carried out to demonstrate that our approach is particularly beneficial in scenarios with multiple privilege sets. Overall, the proposed model offers an elaborate framework that maintains data privacy and strengthens security measures, contributing to a more efficient and secure cloud-based document search. © 2023 The Author(s)

关键词： Cryptography

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Multimodal diagnosis of Alzheimer’s disease based on volumetric and cognitive assessments

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Multimedia Tools and Applications 2025年第17期84卷 18223-18246页

作者： Kumari, Rashmi Das, Subhranil Nigam, Akriti Singh, Raghwendra Kishore School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida201310 India Uttarakhand Dehradun248007 India Birla Institute of Technology Mesra Jharkhand Ranchi835215 India School of Computer Science and Artificial Intelligence SR University Anantasagar Telangana Hasanparthy506371 India

Alzheimer’s Disease (AD) is the most prevalent form of dementia, severely impacting memory functions. Early classification of AD patients, distinguishing them from those with Mild Cognitive Impairment (MCI) or normal cognition (NC), is crucial for clinical intervention. However, traditional methods relying on single MRI feature evaluation yield suboptimal accuracy. Recent advances in multimodal studies show that combining multiple features from various T1-weighted MRI (T1-W1) images can significantly enhance classification accuracy. In this study, we propose a novel Decision Classification Tree (DCT) technique applied to T1-weighted MRI images, incorporating cognitive assessment for global volume measurement. It utilizes morphometric features and spatial information from different Regions of Interest (ROI) within localized brain areas, improving classification accuracy. We evaluate DCT against seven state-of-the- art techniques for binary classification categories (AD vs MCI, MCI vs CN, and AD vs CN) using metrics like accuracy, sensitivity, specificity, and ROC curve analysis. Testing on the ADNI dataset with 102 subjects, we achieve an accuracy of 91.6%, 100% specificity, and 83% sensitivity for the AD vs CN binary class. For MCI vs AD, we attain 76% accuracy, 77.2% specificity, and 80% sensitivity. For MCI vs CN, our model achieves 80% accuracy, 89.4% specificity, and 80% sensitivity. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Magnetic resonance imaging

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