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International Conference on Machine Learning and Big Data Analytics, ICMLBDA 2023

作者： M, Naga Ravindra Babu Durga Satish, M. Prasanthi, B.V. Jagadeesh, S.V.V.D. Janardhana Naidu, J.N.S.S. Pradeep, Immidi Kali Department of Computer Science & Engineering Vishnu Institute of Technology Bhimavaram India

ISBN: (纸本)9783031513374

Today, the governments of developing countries are focusing more on air pollution. Air pollution occurs because of factors like fuel use in vehicles such as private transport, industries, and, more importantly, the burning of waste like plastic and grass. These are a few of the elements that contribute to air pollution. All of these factors have an impact on air quality. It can be calculated or determined by using PM2.5 and other variables. If this level is high, then it may be a serious problem for people’s health. To control air pollution, continuous pollution monitoring is required. Here, different machine learning models like linear regression, decision tree classifiers, random forest classifiers, and KNN classifiers are used to detect whether the sample of air is polluted or not. On the basis of previous readings of PM2.5, NH3, CO, NO, NOx, and NO2, SO2 is used to predict the future values of the AQI. By collecting datasets of the daily atmospheric conditions of a specific city or town, this system is able to predict the PM2.5 level and also detect the air pollution status. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Air quality

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A Comprehensive Visual Debugger For Error Resolution in C++ 15

A Comprehensive Visual Debugger For Error Resolution in C++

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Uke, Shailaja Patankar, Abheerav Patil, Aaditya Patwardhan, Srujan Phand, Swaraj Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350370249

This paper introduces a sophisticated visual debugger that seamlessly integrates C++ code analysis with C # WPF visualisation for an enhanced debugging experience. The tool operates by extracting symbol information from target C++ source files during debugging sessions, utilizing PDB files, specifically tailored for the Visual Studio environment. The core functionality of the tool involves the extraction and storage of symbol information within a meticulously designed stack frame and stack trace classes. Leveraging C++ capabilities, the project captures not only call stack information but also variables and data structures associated with each stack frame, providing a comprehensive view of the program's execution state. To facilitate interoperability and visualization, the project employs a marshalling mechanism to seamlessly transfer the extracted data from the C++ backend to a C# WPF frontend. The Windows Presentation Foundation application serves as an intuitive and interactive interface, presenting the call stack alongside associated variables and data structures in a visually appealing manner. This tool is positioned to significantly enhance the debugging process for developers working in C++ within the Visual Studio environment. By seamlessly integrating symbol extraction, data structuring, marshalling, and visualization, it provides a holistic and efficient solution for diagnosing complex code scenarios. The resulting visualization aids developers in gaining a deeper understanding of their code's execution flow, enabling quicker identification and resolution of issues during the debugging process. © 2024 IEEE.

关键词： Interoperability

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Traffic Density Prediction System using Deep Learning 2

Traffic Density Prediction System using Deep Learning

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2nd International Conference on Data Science and Information System, ICDSIS 2024

作者： Verma, Hritik Sarkar, Sayan Sadhukhan, Souranil Mishra, Aniket Mondal, Md Ashifuddin Narula Institute Of Technology Department Of Computer Science And Engineering India

ISBN: (纸本)9798350381665

In major cities throughout the world, traffic congestion is still a major problem that negatively affects the efficiency of transportation system. Traffic congestion creates several issues that require a creative and adaptable strategy to solve. This work tackles the problem by accurately forecasting vehicle density by merging state-of-the-art deep learning models, specifically YOLO - v8 for real-time vehicles identification and Stacked Long Short-Term Memory (LSTM) models for forecasting traffic density. These methods are combined to create a powerful vehicle density prediction system. The integration of YOLOv8 with LSTM provides a practical response to the growing issues brought on by traffic congestion in cities. The simulation results shows that accuracy of YOLOv8 is 95.36, while average RMSE value of Stacked LSTM models is 0.591. The proposed model is compared with RNN model for vehicle density forecasting and the proposed model performs better than RNN in terms of accuracy. © 2024 IEEE.

关键词： Traffic congestion

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Clinical deep model to analyse medical multivariate time-series data for health diagnosis

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Cyber-Physical Systems 2025年第2期11卷 139-164页

作者： Almahadeen, Layth Vijay, Richa Shabaz, Mohammad Soni, Mukesh Singh, Pavitar Parkash Patel, Pavan Byeon, Haewon Department of Financial and Administrative Sciences Al- Balqa’Applied University Al-Salt Jordan Department of computer science Amity University UP Noida India Department of Computer Science Engineering Model Institute of Engineering and Technology Jammu Jammu and Kashmir India Department of Computer Science Engineering University Centre for Research & Development Chandigarh University Mohali India Department of Management Lovely Professional University Phagwara India Department of Computer Science Engineering Ahmedabad Institute of Technology Ahmedabad India Department of Digital Anti-Aging Healthcare Inje University Gimhae Korea Republic of

Clinical auxiliary decision-making is related to life and health of patients, so the deep model needs to extract the personalised representation of patients to ensure high analysis and prediction accuracy;and provide a basis for prediction conclusions. In this context, a clinical deep model proposed an interpretable assessment method of patient health status based on contextual learning of medical features, encoding the time-series features of each variable separately, and using a multi-head de-coordination self-attention mechanism for learning Relationships between different features;feature skip-connection encoding based on a compressed excitation mechanism is proposed to improve the sensitivity of the model. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Decision making

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A Data-Driven Approach to Crop Yield and Market Price Prediction 8

A Data-Driven Approach to Crop Yield and Market Price Predic...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Phatangare, Sheetal Laddha, Ayush Bambal, Shreya Borhade, Bharati Atram, Prasanna Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350376425

Agriculture, long the foundation of India's economy, currently accounts for less than 15% of GDP due to population increase and the emergence of the industrial and service sectors. Conventional agricultural practices, which mostly rely on farmers' expertise, are becoming less and less effective in handling complicated problems like shifting climatic patterns and the requirement for ideal crop selection. Prediction accuracy and suggestion efficacy of existing models are weakened by issues such inadequate adaptation to varying environmental conditions, limited reactivity to market swings, and limitations integrating real-time data. In order to overcome these shortcomings, this research creates a complex system that uses cutting-edge machine learning methods to improve agricultural decision-making. In order to generate precise estimates of crop production, the system gathers and evaluates data on important aspects such as geographic location, soil quality, and climate characteristics. Additionally, it forecasts market prices using time series analysis, assisting farmers in choosing the best time to sell their produce in order to maximise profits. Farmers can enter, validate, and receive customised predictions and recommendations through an intuitive online application developed with the Flask framework. This creative method boosts production, makes better decisions, and provides a more practical answer to contemporary farming problems, all of which help farmers make more money. © 2024 IEEE.

关键词： Agricultural economics

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Audio Classification Using Ensemble Model

Audio Classification Using Ensemble Model

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2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Gupta, Neelabh Ahlawat, Priyanka National Institute of Technology Department of Computer Engineering Kurukshetra India

ISBN: (纸本)9798350379525

Audio classification is essential for numerous ap-plications, including environmental sound monitoring, speech recognition systems and music genre classification. The ability to accurately classify audio data improves the functionality of these systems, leading to improved user experiences and more effective automation. Accurate audio classification can improve emergency response systems by detecting distress sounds, en-hance smart home devices with better sound recognition and advance healthcare through precise classification of respiratory sounds. Machine learning has transformed audio classification by enabling systems to automatically learn patterns in audio data, enhancing applications like speech recognition and environmental sound monitoring. Convolutional Neural Networks(CNNs), in particular, have proven highly effective for audio classification by extracting hierarchical features from audio spectrograms. This paper proposes an efficient ensemble model involving VGG16, ResNet50 and DenseNet121. The audio data are first converted to spectrograms, which are then used as inputs for the CNN models. The individual pre-trained models are modified to adapt their classifiers to the specific audio classification task, with the number of output classes determined by the dataset. The ensemble model combines the outputs from the three networks, incorporating batch normalization and dropout to improve generalization. By leveraging the complementary strengths of VGG16, ResNet50 and DenseNet121, the ensemble model shows an impressive accuracy of 97.4% on the UrbanSound8K dataset. © 2024 IEEE.

关键词： Speech enhancement

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PerceptiGuard: Unveiling DeepFakes Through Enhanced Media Perception

PerceptiGuard: Unveiling DeepFakes Through Enhanced Media Pe...

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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Phatangare, Sheetal Tekade, Om Thakare, Prathamesh Wankhade, Abhishek Wankhade, Purva Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798331540821

This research presents a comprehensive approach to developing a multi-modal deepfake detection system leveraging advanced deep learning architectures. The primary objectives include the creation of a robust detection model, evaluation across various manipulation techniques, and integration into practical applications. Utilizing state-of-the-art neural networks, the system aims to enhance detection accuracy and offer a user-friendly interface for real-world deployment. The proposed architecture incorporates preprocessing steps such as video splitting, detection, and face cropping, followed by feature extraction and video classification using a combination of ResNet and LSTM networks. The model's performance is assessed through confusion matrix evaluation, ensuring reliability across diverse deepfake scenarios. © 2024 IEEE.

关键词： Generative adversarial networks

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Team IndiDataMiner at IndoNLP 2025: Hindi Back Transliteration - Roman to Devanagari using LLaMa 1

Team IndiDataMiner at IndoNLP 2025: Hindi Back Transliterati...

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1st Workshop on Natural Language Processing for Indo-Aryan and Dravidian Languages, IndoNLP 2025

作者： Kumar, Saurabh Kakadiya, Dhruvkumar Babubhai Singh, Sanasam Ranbir Department of Computer Science and Engineering Indian Institute of Technology Guwahati India

ISBN: (纸本)9798891762145

The increasing use of Romanized typing for Indo-Aryan languages on social media poses challenges due to its lack of standardization and loss of linguistic richness. To address this, we propose a sentence-level back-transliteration approach using the LLaMa 3.1 model for Hindi. Leveraging fine-tuning with the Dakshina dataset, our approach effectively resolves ambiguities in Romanized Hindi text, offering a robust solution for converting it into the native Devanagari script. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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An Experimental Analysis of Machine Learning Model in the Context of Sales Forecasting: -A Walmart Dataset as A Case Study 15

An Experimental Analysis of Machine Learning Model in the Co...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Wankhede, Disha Athanikar, Abhishek Borude, Yash Chougule, Raviraj Diware, Om Department of Computer Engineering Vishwakarma Institute of Information Technology India

ISBN: (纸本)9798331300579

Software programmers may get gradually precise at expecting consequences without being clearly coded using machine learning techniques. Machine learning is based on the idea that models and algorithms may collect input data then utilize statistical investigation to determine an output, while updating results as information become obtainable, as underlying principle. For example, models may be adapted and trained to meet management expectations so that correct measures are followed to reach a certain goal. Wall Mart, a one-stop shopping mall, has been used in this system to estimate sales of various products and to study impact that various variables have on sales of products. Predictive models may be built using different features of a Wall Mart dataset and methods used to construct them, and these findings can be used to make better business choices. This Predictive Algorithm is used in many fields over the market world for their profitability. Recently, Lot of companies are going to used this Machine Learning for better AI and new technologies in market. This report Includes combination of machine learning algorithm and Features engineering to predict Walmart sales. The research topic of this paper primarily solves the problems of Walmart sales forecasting that is provided by the Kaggle competition. © Grenze Scientific Society, 2024.

关键词： Input output programs

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Enhanced Non-Defended Website Fingerprinting Attack Model Using Deep Learning

Enhanced Non-Defended Website Fingerprinting Attack Model Us...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Sinha, Minal Dave, Mayank National Institute of Technology Department of Computer Engineering Kurukshetra India

ISBN: (纸本)9798350375237

Onion routing (Tor) is a routing protocol that uses multiple-layer encryption to protect privacy and security for authentic users during web browsing. The website fingerprinting (WF) attack, which may be used to identify which websites a user is browsing are one of the vulnerabilities present in the Tor browser, despite its security. There are many modern and advanced attack models to build a WF attack model but in this work, Convolutional Neural Network (CNN) is presented considering two different scenarios Close World (CW) and Open World (OW). In CW scenario, the proposed attack model attains 98.20% accuracy on traffic traces without utilizing any defense model and when the same attack model is used in the OW scenario, it attains 97.10% accuracy. In OW scenario, the precision comes out to be 0.99 and the recall is 0.90, even in the presence of imbalanced data without any defenses. Overall, in this work, the proposed non-defended attack model can identify monitored websites with a precision of 0.99, even in the real OW scenario. © 2024 IEEE.

关键词： Websites

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