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2024 International Conference on Computational Intelligence and Computing Applications, ICCICA 2024

作者： Kale, Geetanjali V. Choudhari, Athrava Balapure, Rushi Sarode, Samiksha Wakode, Madhuri S. Pune Institute Of Computer Technology Department Of Computer Engineering Pune India

ISBN: (纸本)9798350306446

This research paper explores the application of computer Vision in the realm of classroom management by addressing the issue of cellphone usage during lectures among students. We present a method for cellphone detection in classrooms during lectures, which can be used to reflect on students' distraction levels. Using CCTV camera recordings from college classrooms, we created two datasets: one comprising standard lecture recordings and the other capturing an empty classroom environment with only tables visible. Our system utilizes two fine-tuned YOLOv8 models, trained on these datasets, to detect and track students' cellphone usage patterns. The table detection model maps out all the tables present in the classroom and the cellphone detection model detects cellphones and their users. The cellphone detection model was trained using three variations of YOLOv8: small (s), medium (m), and large (l). The best performing model was YOLOv8m, trained for 55 epochs, achieving an mAP50 of 89.7%. The integration of computer vision technology into classroom management not only supports educators in enforcing cellphone usage policies but also provides valuable insights for optimizing teaching strategies. Our system facilitates behavior analysis by enabling continuous monitoring of students' cellphone usage over time. Preliminary evaluation of the models shows promising results in accurately detecting and tracking cellphone usage during lectures. © 2024 IEEE.

关键词： Object detection

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Hybrid CGAN-based plant leaf disease classification using OTSU and surf feature extraction

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Neural Computing and Applications 2024年第23期36卷 14395-14407页

作者： Saraswathi, E. Banu, J. Faritha Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai600089 India

Agriculture encompasses a way of life and a profession for the general population. Most global traditions and cultures revolve around agriculture. With the help of advanced farming, agriculture may become more profitable, dependable, and able to use resources and time more effectively. The proposed hybrid CGAN using OTSU and SURF (CGAN-OF) model provides a novel framework for plant leaf disease classification. In proposed CGAN-OF model, contrast-limited adaptive histogram equalization is used for image preprocessing and enhancement. The proposed model utilizes OTSU algorithm to speed up the image segmentation without prior knowledge of the images and SURF algorithm to extract the local features using scale-invariant feature transformation. CGAN increases the input plant village dataset using image generation method and identifies the various plant leaf diseases and classifies them. There are three classifications of leaf diseases: fungi, viruses, and bacteria. Furthermore, these classifications contain approximately 300 diseases. The proposed work identified a minimum of 200 diseases in 18,161 major and minor crop species. The investigational investigations are carried out using the Python Jupyter app with the Kaggle Plant Village Dataset and also leaf samples collected from farmers. The proposed framework achieves 99.2% accuracy. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Image enhancement

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(POSTER) A Holistic IoT-Enabled Approach for Indoor Air Quality Control 19

(POSTER) A Holistic IoT-Enabled Approach for Indoor Air Qual...

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19th Annual International Conference on Distributed Computing in Smart Systems and the Internet of Things, DCOSS-IoT 2023

作者： Kalioras, Fotios Filios, Gabriel Karatzas, Stylianos Nikoletseas, Sotiris University of Patras Department of Computer Engineering and Informatics Greece University of Patras Computer Technology Institute and Press Diophantus Department of Computer Engineering and Informatics Greece University of Patras Department of Civil Engineering Greece

ISBN: (纸本)9798350346497

Indoor air quality (IAQ) is an important yet often overlooked aspect of public health, with poor IAQ contributing to a significant number of diverse health problems worldwide. Existing air quality standards have failed to fully address the problem, however emerging technologies in the field of IoT have demonstrated promise in effectively addressing this issue. This concept paper advocates a hybrid hierarchy system for IAQ monitoring that incorporates both offline and online connectivity. The system utilizes sensing elements that can perform a wide range of functions, including autocalibration, pollution event detection, and more, autonomously from the network. By combining a gateway device, the system's capabilities are enhanced, providing increased data granularity, additional calibration and sensing options as well as connection with Building Management Systems (BMS). Furthermore, by connecting the gateway to the cloud, the system can perform more advanced data analysis and machine learning, providing users with insights into not only air quality but also general environmental quality. Key features of the envisioned system include the integration of subjective perception of pollution via crowdsourcing, a TinyML technology at the edge, digital twins, and an optimised redundancy of sensors that can alleviate poor accuracy and drift as well as capture the spatial dispersion of pollution. © 2023 IEEE.

关键词： Crowdsourcing

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Authorization of Aadhar data using Diffie Helman key with enhanced security concerns

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 8639-8658页

作者： Karthika, K. Rangasamy, Devi Priya Anna University Chennai India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India

In today’s digital era, the security of sensitive data such as Aadhaar data is of utmost importance. To ensure the privacy and integrity of this data, a conceptual framework is proposed that employs the Diffie-Hellman key exchange protocol and Hash-based Message Authentication Code (HMAC) to enhance the security. The proposed system begins with the preprocessing phase, which includes removing noise, standardizing formats and validating the integrity of the data. Next, the data is segmented into appropriate sections to enable efficient storage and retrieval in the cloud. Each segment is further processed to extract meaningful features, ensuring that the relevant information is preserved while reducing the risk of unauthorized access. For safeguarding the stored Aadhaar data, the system employs the Diffie-Hellman key exchange protocol which allows the data owner and the cloud service provider to establish a shared secret key without exposing it to potential attackers. Additionally, HMAC is implemented to verify the identity of users during the login process. HMAC enhances security by leveraging cryptographic hash functions and a shared secret key to produce a distinct code for each login attempt. This mechanism effectively protects the confidentiality and integrity of stored data. The combination of Diffie-Hellman key exchange and HMAC authentication provides a robust security framework for Aadhaar data. It ensures that the data remains encrypted and inaccessible without the secret key, while also verifying the identity of users during the login process. This comprehensive approach helps preventing unauthorized access thereby protecting against potential attacks, instilling trust and confidence in the security of Aadhaar data stored in the cloud. Results of the article depict that the proposed scheme achieve 0.19 s of encryption time and 0.05 s of decryption time. © 2024 – IOS Press. All rights reserved.

关键词： Sensitive data

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Document Query Processing With Mistral 7B and Streamlit 8

Document Query Processing With Mistral 7B and Streamlit

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8th IEEE International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Saraf, Renuka Kolhe, Gopalkrushna Badjate, Soham Gadekar, Rohit Joshi, Pranjali Pune Institute of Computer Technology Computer Engineering Department Pune India

ISBN: (纸本)9798350391770

PDF's form an integral part in day to day life of every individual. Researchers, scientists, teachers and students need to read and go through tons of PDF in order to gain information. This is an overhead for the people. In past there have been multiple attempts to get the information from the PDF's by directly asking the question. But most of the approaches are limited to single PDF and the accuracy is a concern with respect to the context in which answer is generated. In this paper we propose an approach that used Mistral 7B model to generate the answers to the questions asked by the user on multiple PDF's uploaded at once. We also evaluate our model against the existing approaches to answer generation and demonstrate a better approach to solve the problem. Upon analysis we arrive at a conclusion that Mistral-7B outperforms other LLMs for this task. By incorporating prompt lookup technique, the response time was improved by approximately 80%. © 2024 IEEE.

关键词： Query languages

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Subjective Answer Grader using Semantic Similarity and Keyword Matching 9

Subjective Answer Grader using Semantic Similarity and Keywo...

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

作者： Kripalani, Sanchit Lipare, Tejas Kadam, Dipali Pune Institute of Computer Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350394474

Subjective exams and tests have been a staple metric of student evaluation. These tests require students to give brief theoretical responses to the questions. Evaluation of subjective responses is based on the presence of certain facts, keywords, phrases that need to be mentioned. Traditional assessment of these tests requires an examiner to affirm the understanding of concepts by students through their answers. The human-dependant evaluation, however, is tedious, time consuming and is rarely impartial. The time constraints often mean that the examiner is unable to provide a detailed feedback to each and every student. This calls for the need to develop an objective automated grader system, which can investigate and analyze the student performances by considering different facets of learning based on semantic similarity. The enhanced BERT model, which has been fine-tuned, is utilized to capture the semantic characteristics found in the student's response. In order to achieve this, the model compares the student's response with the model answer and generates a model score based on the comparison. This model score is boosted through a keyword matching algorithm, which emphasizes the factual information contained within the student's response. These final similarity scores are employed to offer comprehensive feedback that illuminates the specific areas in which the student's comprehension may be lacking. Finally, the proposed approach is compared to other renowned algorithms like Cosine Similarity, Word Mover's Distance, Paraphrase-mpnet, and Multi-qa-mpnet. The proposed system yields an experimental Mean Absolute Error (MAE) of 2.11, which is significantly better as compared to other algorithms. © 2024 IEEE.

关键词： Grading

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Ensemble activation enabled deep classifier for Alzheimer's disease detection in the blockchain-enabled distributed edge environment

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International Journal of Information technology (Singapore) 2024年 1-18页

作者： Mandawkar, Umakant Diwan, Tausif Department of Computer Science and Engineering Indian Institute of Information Technology Nagpur India Department of Computer Engineering SVKM’s Institute of Technology Maharashtra Dhule India

Alzheimer's disease (AD) is the most well-known cause of dementia that affects memory. Alzheimer's patients have a neurodegenerative disorder that results in the loss of many brain functions. Today’s researchers have determined that the key to enhancing patient care and treatment is early disease diagnosis. Traditional techniques for diagnosing AD are difficult ineffective, and take a lot of time to learn and execute. In this research, a disease detection system is developed using the proposed tawny flamingo-based federated deep CNN (Convolutional Neural Networks) (TF based-deep CNN) which can manage accurate patient diagnosis in dispersed contexts with blockchain support. The collected data is maintained and structured in a blockchain-based database, which upholds a high level of data security and increases the detection accuracy. After the medical professional receives the data, disease detection is done using the proposed federated learning framework, which makes it feasible to manage remote data effectively. The features are then extracted from the segments created by the CUF-GW (Cuttlefish-GreyWolf) algorithm. The detection is then carried out by the proposed ensemble activation-based deep CNN classifier, with the ensemble process being assisted by the TF (Tawny Flamingo) approach. According to AD accomplishments, at TP (Training Percentage) 80, acc (Accuracy), sen (Sensitivity), and spe (Specificity) are respectively 99.19%, 99.99%, and 98.87%;similarly, based on the k-fold 10, acc, sen, and spe are respectively 99.45%, 99.99%, and 98.45%. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Alzheimer's disease CUF-GW algorithm Deep CNN Federated learning TF

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Automated Answer and Diagram Scoring in the STEM domain: A literature review 5

Automated Answer and Diagram Scoring in the STEM domain: A l...

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5th IEEE International Conference for Emerging technology, INCET 2024

作者： Kulkarni, Aditya Endait, Sharvi Ghatage, Ruturaj Patil, Rajlaxmi Kale, Geetanjali Department of Computer Engineering Pune Institute of Computer Technology Pune India

ISBN: (纸本)9798350361155

Automated scoring of answers and diagrams in the STEM (Science, technology, engineering, and Mathematics) domain has gathered significant attention in recent years due to its potential to revolutionize assessment and feedback processes. This survey paper presents an exhaustive analysis of the automated multimodal scoring methods for answers and diagrams applied within the STEM educational context. It delves into the diverse techniques, applications, challenges, and prospects within this burgeoning field. The survey highlights the evolving technologies and tools, offering insights into the platforms and systems that have been developed to automate the evaluation of answers and diagrams in STEM subjects. Additionally, this survey provides a valuable resource for educators, researchers, and policymakers seeking a comprehensive understanding of the current landscape of automated multimodal scoring in STEM education. It aims to inform discussions on the development and implementation of effective assessment strategies in STEM fields and the role of technology in enhancing educational outcomes. © 2024 IEEE.

关键词： Data integration

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An Empirical Survey on the Classification Strategies for Automated Alzheimer’s Disease

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SN computer Science 2024年第8期5卷 1140页

The early Alzheimer’s disease (AD) classification is highly important as this neuro-degenerative disease causes severe problems particularly, loss of memory among the patients. In addition, classifying Normal Control (NC), Mild Cognitive Impairment (MCI) and AD in time assists the patient to undergo treatments in the initial period of the disease. Hence, it is necessary to develop a trust worthy categorization model to classify the patients with or without AD. One of principal modalities commonly used for AD classification is the magnetic resonance imaging (MRI), the analysis of which achieved improved classification performance in categorizing the stages of AD. Hence, this review paper presents the detailed survey of 55 research articles showing different strategies to classify AD. The two important categories of the research papers are the methods using machine learning strategies, and the methods using deep learning strategies. The papers are classified based on various modalities, datasets, performance indices, and the software tools used for implementation. In addition to this, various research gaps and the challenges associated with the existing works of AD classification are discussed. In addition, this review provides the future scope for researchers with the analysis of research issues seen in the literary works. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Alzheimer’s disease Deep learning and machine learning Magnetic resonance imaging Mild cognitive impairment

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Exploiting Retina Biometric Fused with Encoded Hash for Designing Watermarked Convolutional Hardware IP Against Piracy

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SN computer Science 2024年第8期5卷 1-17页

作者： Chaurasia, Rahul Sengupta, Anirban Department of Computer Science and Engineering Indian Institute of Information Technology Bhopal Bhopal India Department of Computer Science and Engineering Indian Institute of Technology Indore India

The convolution layer in a convolutional neural network (CNN) is highly computationally intensive. It is crucial to design reusable low-cost hardware IP for convolutional layer for enabling hardware-based feature extraction. However, the involvement of fake IP vendor/untrustworthy broker in the integrated circuit (IC) supply chain, makes these IPs susceptible to the threat of piracy. The proposed approach presents high- level synthesis (HLS) driven watermarking methodology for designing low-cost and secure convolutional hardware IP. The presented watermarking approach employs complier-driven high-level transformation and exploits retinal signature fused with the encoded hash for piracy detective countermeasure. The proposed approach, therefore, firstly performs compiler-driven high-level transformation in order to optimize the design latency, followed by embedding the watermark of an authentic IP vendor. The generated watermark in the form of encoded hardware watermarking constraints (digital evidence) is covertly embedded into the resulting optimized design during the register allocation module of HLS. The proposed approach achieves the following: (i) optimized and secure design for convolutional hardware IP, (ii) robust detection of pirated IP at zero design cost overhead, (iii) significantly lower probability of coincidence (in the range of 1.3E−06 to 1.2E−09) indicating stronger digital evidence and higher tamper tolerance (in the range of 2.64E+460 to 9.60E+698) than recent approaches. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional layer Encoded hash Hardware security HLS IP design Retina biometrics

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