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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Sharma, Rupak Tyagi, Pallavi Vajpayee, Pooja Gangwar, Deepti Tyagi, Umang Saraswat, Birendra Kumar Srm Institute of Science and Technology Department of Computer Applications NCR Campus UP Modinagar India Raj Kumar Goel Institute of Technology Computer Science & Engineering UP Ghaziabad India Galgotias University School of Computer Science and Engineering Greater Noida India Raj Kumar Goel Institute of Technology Department of Ece UP Ghaziabad India

ISBN: (纸本)9798331508685

Deep learning is a robust framework for solving complex prediction and forecasting problems from multiple fields, such as weather sciences. Due to the abundance and higher resolution of data available, cloud-based deep learning offers a compelling approach for achieving promising gains in accuracy and efficiency regarding weather forecasting. A more pressing priority in the face of worsening weather events requires faster and more reliable forecasting. Deep learning in the cloud is implemented by training deep neural networks on large datasets stored in a public or private cloud and subsequently using these pre-trained models to predict new data. This encourages using large-scale computational resources, such as those necessary to tackle the heavy data needed for precise meteorological forecasts. Such deep learning models have the flexibility and scalability required to adapt and tune them for specific weather patterns, which can then run on a cloud, enabling readiness as demanded. In this work, 3, x, y present a scalable deep learning-based approach to extended-range temperature prediction on high-performance cloud resources. We use the model to train on historical weather data combined with real-time observed weather from various sources. © 2025 IEEE.

关键词： Prediction models

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An Ensembled Grid based Machine Learning Approach For PD Classification From MRI Images 4

An Ensembled Grid based Machine Learning Approach For PD Cla...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Redhya, M. Jayalakshmi, M. Kalasalingam Academy of Research and Education Department of Computer Applications Tamil Nadu India Kalasalingam Academy of Research and Education School of Computing Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798350343670

Parkinson's disease (PD) is a neuro-degenerative disease caused due to breakdown of brain cells in the central-part of the nervous system. As symptoms of PD appear only after 60% or more of these cells are destroyed, early detection is a tedious task. Several studies have proven that PD can be effectively diagnosed from MR-Images (MRI). This paper provides an ensembled machine learning model, Stacked-EG model, for MRI-image based PD classification. The dataset was obtained from PPMI (Parkinson's Progression Markers Initiative) online repository which included 134 PD subjects (80 males and 54 females) and 126 healthy patients (60 males and 66 females) around 60 years old on average. Feature extraction is performed through the application of filters, including wavelet transformations and intensity-based enhancements. Feature selection is conducted using optimised XGBoost model's feature significance scores. For classification, a Stacking Classifier which combines the strengths of XGBoost, SVM, and a Random Forest classifier was used. The model obtained an accuracy of 98.41% in classifying PD from healthy controls. © 2024 IEEE.

关键词： GridSearch Optimization Magnetic Resonance Imaging (MRI) Random Forest Stacking SVM XGBoost

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Using Machine Learning Techniques in Business Analytics: Lessons from Agile Project Management 7

Using Machine Learning Techniques in Business Analytics: Les...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Kaur, Harjeet Sarkar, Tiyas Kaur, Baljinder Singh, Amanpreet Cheema, Gagandeep Singh Das, Sumitra Lovely Professional University School of Computer Science & Engineering Punjab Phagwara India Lovely Professional University School of Computer Science & Engineering Uttarakhand Dehradun India Lovely Professional University School Of Computer Science & Applications Punjab Phagwara India Lovely Professional University Mittal School of Business Punjab Jalandhar India

ISBN: (纸本)9798350350067

A potent technique for drawing conclusions from data and advancing corporate analytics is machine learning (ML). Although successful ML project implementation calls for more than simply academic know-how;it also calls for agile approaches, efficient project management, and strategic planning. This study examines the relationship between agile project management and machine learning adoption, offering suggestions for how to include ML throughout business data analysis projects. In the larger context of business analytics, we point out important factors, obstacles, and tactics for streamlining the value of machine learning initiatives by referencing agile methodology principles and best practices. This paper seeks to provide enterprises with the expertise and resources needed to effectively employ machine learning (ML) for business analysis and gain a competitive edge by outlining efficient implementation methodologies. © 2024 IEEE.

关键词： agile project management and implementation strategies business analysis data science predictive modelling Project success

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A Music Recommender System to Identify the Emotional State of a Person Using Machine Learning Algorithms 4

A Music Recommender System to Identify the Emotional State o...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Muruganantham, A. Malarvizhi, C. Divya Vahini, S. Saranya, D. Uthiramoorthy, A. Karnataka Bengaluru India Psg Institute of Advanced Studies Department of Computer Science Coimbatore India Dr.Mahalingam College of Engineering and Technology Department of Computer Applications Tamil Nadu Pollachi India Sri Eshwar College of Engineering Department of Information Technology Coimbatore India Rathinam College of Arts and Science Department of Computer Science India

ISBN: (纸本)9798331523923

In today's digital world, customized recommendation algorithms can improve user experiences in many different areas. Within the sphere of intelligent human-technology interaction, this paper specifically examines a music recommender system driven by machine learning algorithms. It figures out what mood users are in and can provide an appropriate response in real time. The system being propounded in this essay uses advanced technology in facial expression recognition. With the help of a webcam, it aims to accurately detect user emotions, including joy, sadness, and anger. Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Support Vector Machines (SVM) have been employed to effectively classify people's mood and recommend an appropriate song. Our extensive tests and examinations show that CNN, RNN, and SVM models are all very good at guessing users' moods - with high precision, recall, F1-score, and accuracy percentages. Furthermore, in-depth analyses like confusion matrixes illuminate how well each model serves as an emotion recognizer. This study's findings are representative of future prospects in affective computing around the globe with major implications for intelligent recommendation systems to which it's important that the models are sensitive. In conclusion, the suggested Music Recommender System is something new for taking a more personal view: We can be as deeply entertained as the web allows us to be - make tailored entertainment paths in the digital era. © 2025 IEEE.

关键词： Convolutional neural networks

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Enhancing digital security using Signa-Deep for online signature verification and identity authentication

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2024年第2期8卷 58-69页

作者： Ch, Ravikumar Sridevi, Mulagundla Ramchander, M. Ramesh, Vankudoth Kumar, Vadapally Praveen Department of Artificial Intelligence &Data Science Chaitanya Bharathi Institute of Technology Hyderabad 500075 India Department of Computer Science and Engineering CVR College of Engineering Hyderabad 501510 India Department of Computer Applications Chaitanya Bharathi Institute of Technology Hyderabad 500075 India Department of Emerging Technologies CVR College of Engineering Hyderabad 500039 India

In the contemporary digital realm, the utilization of online services has surged, facilitated by the seamless integration of deep learning technology, which is paramount in applications demanding precision and efficiency. A pivotal use case in this context is online handwritten signature verification, where the need for exceptional accuracy is indisputable. This paper introduces ‘Signa-Deep, ’ an innovative approach designed to address the challenge of online signature verification and the determination of an individual’s authorization status. The study explores a range of methodologies, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), GoogleNet, and MobileNet, to discern the authenticity of signatures and affirm the identity of the signatory. The results of our proposed method are promising, showcasing its potential to significantly enhance the security of digital transactions and identity verification processes. In summary, ‘Signa-Deep’ harnesses deep learning technology to bolster the accuracy and reliability of online signature verification, thereby contributing to the overall robustness of digital interactions and identity validation processes. © (2024), (Society of Sytematic Innovation). All rights reserved.

关键词： Authorization Status Deep Learning Digital Transactions Security Identity Authentication Online Signature Verification

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AMBTC-Based High Capacity Data Hiding Scheme Exploiting PVD and BRP 3rd

AMBTC-Based High Capacity Data Hiding Scheme Exploiting PVD ...

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3rd International Conference on Advanced Computing and applications, ICACA 2024

作者： Jana, Manasi Jana, Biswapati Joardar, Subhankar Department of Computer Applications Haldia Institute of Technology West Bengal Haldia India Department of Computer Science Vidyasagar University West Bengal West Midnapore India Department of Computer Science and Engineering Haldia Institute of Technology West Bengal Haldia India

ISBN: (纸本)9789819747986

Secret data hiding in compression domain reduces the transmission cost and also ensures more security than spatial and transform domain. In this paper, we present a data concealing strategy on the compression domain that delivers high embedding capacity (EC) while also improving the quality of transformed images. To compress the original image, a modified AMBTC method is used for its low time complexity. Modified AMBTC is used to transform one bit plane into two bit plane, which enhances the quality of the stego image. Three levels of security are applied on secret data to make the scheme more secure. Data are embedded into two adjacent pixels of each 1×2 block using PVD method. The robustness of the scheme is analysed by applying unknown permutation, unknown shared key and unknown iteration steps. The experimental results reveal that the suggested method outperforms the previous schemes in terms of embedding capacity and imperceptibility. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Ambtc BRP Data hiding PSNR PVD

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Class Room Student Attentiveness Model based on YOLO 14

Class Room Student Attentiveness Model based on YOLO

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

作者： Nair, Rekha R. Babu, Tina Singh, Tripty Duraisamy, Prakash Mehra, Vishal Dayananda Sagar University School of Engineering Department of Computer Applications Bengaluru India Dayananda Sagar University School of Engineering Department of Computer Science and Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India University of South Alabama Department of Computer Science United States

ISBN: (纸本)9798350335095

Online classroom discipline is a key concern for teachers now a days. The issue also attracts significant attention from the public and the media. In addition to this using mobile phone in class is one of the reasons for not paying the attention. For avoiding this issue, developed a model called "Class Room Attention Model". The proposed model collects the data using camera, and with the help of machine learning models automatically detects whether the student in the class is paying attention or not. The suggested method uses Tensor Flow to detect the mobile phone and media pipe for extracting the body features using the YOLO model. The model detects presence of mobile in the classroom in addition to the prediction of the students attentiveness. The proposed efficient and accurate model to identify the attention of a student in a class room with the random forest classifier provided an accuracy of 97.65 % for attention of student and 93.81% for mobile detection. © 2023 IEEE.

关键词： Students

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Predictive Analytics for Stroke Prevention: A Machine Learning Perspective 10

Predictive Analytics for Stroke Prevention: A Machine Learni...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Mattas, Puranjay Savar Mishra, Pradeep Kumar Singh, Himanshu Dev School of Engineering and Technology Sharda University Department of Computer Science and Applications Uttar Pradesh Gr. Noida India

ISBN: (纸本)9789380544472

The objective of this study is to develop a machine learning model (ML) to obtain the risk of stroke in patients. We used a dataset of 4,981 patients to train and test the model. Decision Tree Classifier (DTC) was used as the ML model, which is a widely used algorithm for classification problems. Before training the model, we cleaned the dataset by removing inaccurate and incomplete data to ensure that the model is not fed with faulty data. This confirms that the model can make accurate predictions based on the available data. After cleaning the data, we trained the DTC model and tested it on the same dataset. Our model performance shows that this model can accurately predict the risk of stroke in patients with high accuracy. The outcome of this study can be beneficial for both patients and medical professionals. Patients can be informed of their risk of stroke and can take preventative measures to reduce their risk. In conclusion, we successfully developed an ML model using a Decision Tree Classifier to obtain the risk of stroke in patients. Our study demonstrates the potential of ML in healthcare and its application in predicting the occurrence of diseases. This article can serve as a foundation for future research on improving the accuracy of stroke risk prediction models, which can lead to better patient outcomes and improved healthcare practices. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Machine learning

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Plastic Waste Classification Using CNN for Supporting 3R's Principle 12

Plastic Waste Classification Using CNN for Supporting 3R's P...

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12th International Conference on computer Communication and Informatics, ICCCI 2022

作者： Gothai, E. Thamilselvan, R. Natesan, P. Keerthivasan, M. Kabinesh, K. Ruban, D. Kevin Computer Science and Engineering Kongu Engineering College Perundurai India Computer Applications Kongu Engineering College Perundurai India

ISBN: (纸本)9781665480352

Plastic isn't as simple as everyone assume. The world now produces more than 380 million tons of plastic, half of this is utilized to make one-use items such as polythene bags, mugs, and straws. Every year, at least 8 million tons of plastic wind up in our oceans, which end up as pollutants, entering our natural environment. Thus, it is necessary to handle such kind of massively produced material. There are 7 major categories of plastics. Every single one of them is distinct from the others. Some are reusable, but others could leach harmful compounds if exposed to extreme temperatures. Some materials are easily recyclable, while others require more complex and delicate processing during the recycling process. Although scientists are actively striving to develop the optimum method and strategy for recycling all of those forms of plastic, recycling programs mostly accept Polyethylene Terephthalate (1-PET) and High-Density Polyethylene (HDPE) (2-HDPE). This classification of plastics for recycle, reuse or reduce is a necessary thing to avoid the greenhouse effect and ozone layer depletion, which Is a difficult thing to achieve through manual power. The main idea is to reduce the waste and to recycle and promote the reuse of the same materials while lowering the cost of processing them. Thus, by combining the secondary use of raw materials with a change in their condition and composition, consumer trash may be *** fundamental issue addressed in this article is the development of an automatic technique for sorting plastic *** necessitates sorting waste into fractions other than metal, bio, plastic, paper, and glass. Because not all of the components in each group are acceptable for reuse today, advanced procedures must be used to distinguish the type of material in each group This can sort into the four categories listed above, PS, PP, PE-HD, and PET, and can be used in a sorting factory or at home by residents. Plastic categorization by hand is a time-co

关键词： Polyethylene terephthalates

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Leveraging Machine Learning for Early Detection of Cardiovascular Diseases

Leveraging Machine Learning for Early Detection of Cardiovas...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Shrivastava, Anurag Maindola, Meenakshi Kumar, Rakesh Thethi, H. Pal Sreeja, S.P. Sirisha, N. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India GLA University Department of Computer Engineering & Applications Mathura India Lovely Professional University Phagwara India New Horizon College of Engineering Master of Computer Application Bangalore India MLR Institute of Technology Department of Computer Science & Engineering Telangana Hyderabad India

ISBN: (纸本)9798331512965

In order to enhance patient outcomes, rapid and precise detection approaches are needed for cardiovascular diseases (CVDs), which are among the top causes of death globally. By examining a wide range of demographic and clinical data, this study investigates how machine learning approaches could improve the early diagnosis of CVDs. We explore how many different machine learning methods could identify different heart diseases. Decision trees, support vector machines and more, are included in these algorithms. By using feature selection and optimization methods we want to strengthen these models' prediction abilities and make them more resilient. The dataset used by this research includes all of the medical information of patients as they include all the pertinent biomarkers such as age, blood pressure, cholesterol levels and etc. The results suggest that machine learning models, especially ensemble methods like Random Forest and gradient boosting, are indeed able to predict the risk of CVD better than conventional diagnostic strategies. Results suggest that early risk assessment using machine learning embedded within healthcare processes is a reliable and non-invasive approach. This study has found machine learning to have promise for change and further research into the practical use of these models to revolutionize treatment of cardiovascular diseases. © 2025 IEEE.

关键词： Risk assessment

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