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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Humbe, Siddharth Lavekar, Piyush Kapadnis, Nikita Choudhary, Durgesh Lokhande, Ganesh Pune Institute of Computer Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798331505264

Video fingerprinting has come out to be one of the crucial technologies for multimedia content recognition, copyright protection and management. This review puts flashlight on the recent advancements on various video fingerprinting techniques such as spatial, temporal, and audio- based methods, with given more focus on their applications, strengths and limitations. A structured process to search for, select and analyze relevant references were carried out during the process in which analyzed various articles from databases such as IEEE Xplore and Google Scholar. This review talks about how different methods that have been used in video fingerprinting, making it more accurate. However, this paper makes comparison of previously known methods based on different parameters. The review made rom different suggests future research that should be focused on creating hybrid models and these models should aim to find a good balance between how well they work and how much resources they use. © 2024 IEEE.

关键词： Deep learning

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ICode-An Unified Competitive Coding Profile Platform 6

ICode-An Unified Competitive Coding Profile Platform

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6th IEEE International Conference on Emerging Smart Computing and informatics, ESCI 2024

作者： Muke, Saurav Ahire, Samruddhi Kale, Geetanjali Navghare, Pranali Pune Institute of Computer Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350306613

Although a software developer is capable of creating excellent software without the necessity for competitive coding. Having their hands in it may increase the project's efficiency and scalability. For every developer, problem solving is a must-required skill that is gained through competitive coding. Many technical recruiters use competitive programming as a barometer to assess a candidate's potential for the post of software engineer, including many prestigious Silicon Valley companies like Google, Facebook,etc. Since there is no such platform that helps students showcase their coding status on every registered platform, the proposed system helps to have such. ICode is a unified platform to help students visualize their coding profile status on a single site at one go. There is no public API that can be used to retrieve the *** websites;hence, web scraping becomes relevant in this situation. It is one of the strategies of information *** of manually copying data from the browser, with web scraping, it becomes easy to automatically extract the data. without any effort and perform analysis on it. The paper mainly targets the visualization of the data scrapped from competitive coding platforms to create a digital resume and perform analysis based on the scraped data. This is a digital *** be a revolution in the modern-day recruitment process since it will contain realtime user's details and coding platform ratings. and their analysis, project information, and digital bridges to platforms like Github and LinkedIn, where recruiters can actually review the users's information in a better way as compared to the old paper resume. © 2024 IEEE.

关键词： API data data analysis retrieval visualisation web scraping websites

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Identification of Software Bugs by Analyzing Natural Language-Based Requirements Using Optimized Deep Learning Features

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computers, Materials & Continua 2024年第3期78卷 4379-4397页

作者： Qazi Mazhar ul Haq Fahim Arif Khursheed Aurangzeb Noor ul Ain Javed Ali Khan Saddaf Rubab Muhammad Shahid Anwar Department of Computer Science and Engineering and International Bachelor’s Program in Informatics Yuan Ze UniversityZhongliTaoyuan320315Taiwan Department of Computer Science National University of PakistanRawalpindiPunjab46000Pakistan Department of Computer Software Engineering MCSNational University of Sciences and TechnologyIslamabad44000Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Department of Computer Science School of PhysicsEngineering and Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK Department of Computer Engineering College of Computing and InformaticsUniversity of SharjahSharjah27272United Arab Emirates Department of AI and Software Gachon UniversitySeongnam-SI13120South Korea

Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty *** are exploring machine learning to predict software bugs,but a more precise and general approach is *** bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning ***,these studies are not generalized and efficient when extended to other ***,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification *** methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a *** National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve(AUC-ROC)values when different classifiers were *** reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode.

关键词： Natural language processing software bug prediction transfer learning ensemble learning feature selection

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Adaptive Attention Mechanisms Based Multicriteria Recommender Systems for Improved User Preference Discovery

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SN computer Science 2025年第4期6卷 1-16页

作者： Rajput, Ishwari Singh Tewari, Anand Shanker Tiwari, Arvind Kumar Department of Computer Science & Engineering National Institute of Technology Patna Bihar Patna India Department of Computer Science & Engineering Kamla Nehru Institute of Technology Sultanpur (UP) India

Personalized recommender systems are becoming more popular to reduce the issue of information overload. It is also observed that the recommendations provided by multi-criteria recommender system (MCRS) are more accurate and precise in comparison to conventional single-criteria recommender systems. MCRS incorporates customers’ preferences based on multiple criteria while recommending any item. Recent research has shown that MCRS can efficiently use implicit and explicit input across various dimensions to make exact recommendations across multiple domains. Due to use of multiple criteria, existing MCRS methods often struggle with effectively aggregating and interpreting these criteria, making it hard to accurately determine user preferences. In this article, we introduce a novel adaptive attention mechanism that dynamically assigns weights to each criterion based on user preferences. Each criterion is initially weighted based on its importance to a user. An attention mechanism is used to understand the exact user’s preference for each criterion. Later, the nearest-neighbor method finds users with similar interest to predict unknown ratings which is further used for Top-N recommendations. The performance of the proposed approach (AM-MCRS) is validated on Yahoo! movies multi-criteria dataset having 1716 unique users and 965 different movies and a comparative analysis is performed with existing single and multi-criteria recommender systems. The results clearly show that the AM-MCRS methodology outperform baseline methods, achieving the highest precision (0.9412), F-score (0.9343), accuracy (0.929), and coverage (0.9407), while also observing a significant reduction in MAE (0.7623) and RMSE (0.9145). These findings highlight the capability of AM-MCRS to improve recommendation accuracy and dependability across various domains. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Attention mechanism Deep learning Dynamic user preference Multi-criteria recommender system Nearest-neighbor

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A Brief Survey on Video Stabilization Approaches 5

A Brief Survey on Video Stabilization Approaches

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5th IEEE International Conference on Recent Trends in computer Science and technology, ICRTCST 2024

作者： Kamble, Samarth Dixit, Mrunmay Nehete, Dinesh Joshi, Dhara Potdar, G.P. Madhuri Mane, P. Pune Institute of Computer Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350351378

Video stabilization is an important aspect of digital video processing, which addresses the challenge of reducing unwanted motion-induced artifacts in video recordings This research paper presents video provides a detailed overview of stability techniques from classical methods to recent developments. We classify these methods into several different categories, including feature-based methods, visual flow-based methods, and deep learning-based methods. Each category is examined in detail, covering the underlying principles, a story of outstanding strengths, weaknesses, and *** addition, we discuss the practical applications of video stabilization in various industries, highlighting its important role in film-making, action cameras, surveillance, medical imaging, and more. In addition to the systematic classification and analysis of existing methods, this study highlights recent developments and emerging trends in the field. A comparative study of different stability methods highlights their relative efficacy and computational requirements to help researchers and practitioners choose the most appropriate method for their particular application Furthermore, we address current challenges in deals with video stability and we present possible directions for future research, opening the way for continued innovation in this dynamic field. © 2024 IEEE.

关键词： Video recording

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Real-time Detection of Students' Cellphone Usage During Lectures and Behavioral Analysis

Real-time Detection of Students' Cellphone Usage During Lect...

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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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Comparative Analysis of Machine Learning Algorithms for Cardiovascular Disease Risk Prediction 1

Comparative Analysis of Machine Learning Algorithms for Card...

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1st International Conference on Trends in engineering Systems and Technologies, ICTEST 2024

作者： Doiphode, Anushka Bora, Karan Navghare, P.R. Pune Institute of Computer Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350307795

Cardiovascular conditions (CVDs) remain a major global health concern, challenging early threat assessment and forestalment. In this study, we employ four distinct machine learning algorithms - K Nearest Neighbors (KNN), Random Forest, Decision Tree and Support Vector Machine - to prognosticate the threat of cardiovascular conditions. The dataset used in this exploration consists of 14 essential features and has been sourced from Kaggle, a prominent platform for data wisdom competitions. Our primary idea is to estimate the prophetic performance of these algorithms and determine which algorithm gives us maximum accuracy. Among the algorithms examined, the KNN classifier emerges as the top pantomime, achieving an accuracy of 84.48. This result showcases the efficacy of the KNN algorithm in handling the complexity of CVD threat vaticination. These findings provide critical guidance for healthcare professionals and experimenters in enforcing effective prophetic models for early cardiovascular complaint threat identification and intervention. © 2024 IEEE.

关键词： Decision trees

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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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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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(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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