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AI-Driven Prediction Model for Forecasting the Spread and Peak of COVID-19 using Epidemiological data 3

AI-Driven Prediction Model for Forecasting the Spread and Pe...

引用

3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Muppawar, Harsh Pacharney, Utkarsha Datta Meghe Institute Of Higher Education And Research Departmrnt of Artificial Intelligence and Data Science Wardha India

ISBN: (纸本)9798331509675

The COVID-19 pandemic, caused by the virus SARS-CoV-2, has brought unprecedented challenges to all public health systems in the world. Accurate spreading prediction and the time of its peak is crucial for appropriate responses and the allocation of resources. This research study proposes an AI-based forecasting model with deep learning techniques, especially LSTM networks, for the analysis of epidemiological data aimed at forecasting the trajectory of the pandemic. The model identifies trends and predicts future outbreaks with high precision by integrating time-series data on infection rates, recoveries, fatalities, and testing rates. The performance of the model is compared against traditional methods in this study, and it clearly shows that it has better accuracy in dynamic and real-time scenarios. Scenario simulations with interventions such as lockdowns and vaccination campaigns show the value of the model in policy decision-making. While the model had issues in terms of data quality and socio-economic variability, adaptability as well as the predictive power underscore the revolutionary role that AI plays in public health. This research furthers the understanding regarding applications of AI in epidemiology: it offers a robust tool for managing COVID-19 and future health crises. © 2025 ieee.

关键词： COVID-19

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Generative AI for Cultural Heritage Preservation Using AR and data science 2

Generative AI for Cultural Heritage Preservation Using AR an...

引用

2nd international conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Mogre, Sakshi C. Nikam, Jay S. Damani, Punit Banik, Abira St. John College of Engineering & Management Department of AIML Mumbai India

ISBN: (纸本)9798331505745

Cultural heritage artifacts are invaluable to understanding our shared history and cultural identity. However, these artifacts are increasingly at risk due to aging, environmental factors, activities and humans. This paper explores the integration of Generative AI, augmented reality (AR), and data science in cultural heritage preservation. Generative AI facilitates the restoration of damaged or missing parts of artifacts, while AR provides immersive, interactive experiences, enhancing accessibility and engagement. data science contributes by analyzing historical and environmental data to inform conservation strategies. This innovative approach demonstrates the potential to transform cultural heritage preservation, ensuring sustainability and accessibility for future generations. © 2025 ieee.

关键词： Historic preservation

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Hybrid data Fusion for Elegant Emotion Recognition in Cloud-Based Big data systems

Hybrid Data Fusion for Elegant Emotion Recognition in Cloud-...

引用

2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Jin, Hulin Sun, Xun Jin, Zhiran Kim, Yong-Guk School of Computer Science and Technology Anhui University Hefei230031 China Foothill Preparatory School Temple CityCA91780 United States Sejong University Department of Computer Engineering Seoul05006 Korea Republic of

ISBN: (纸本)9798331509828

In the era of intelligent information, video, audio, and text information are growing explosively. How to extract emotional factors from this information has become very important. This study proposes the novel hybrid data fusion framework based on cloud computing big data system for elegant emotion recognition. This model can effectively solve the multimodal data fusion and recognition tasks. Initially, as the cloud system are always encrypted, data verification mechanism based on linked list index structure is integrated and the novel data extraction methodology is designed. In the image emotion recognition (IER) part, a structure based on a lightweight neural network is proposed, and combined with the Transformer model, spatial and temporal features are extracted to enhance the accuracy of the emotion recognition. In the audio emotion recognition (AER) part, an innovative emotion vector space analysis method is proposed, which converts the emotional expression in music into vector representation and uses the Transformer encoder structure to capture temporal relationship in music features. In the hybrid data fusion aspect, this study uses the Lukasiewicz T-norm to achieve the fusion of image and audio data. Through the experiment, recognition accuracy is tested and performance is evaluated. © 2025 ieee.

关键词： Spatio-temporal data

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Automation of IoT Network Traffic Analysis and Anomaly Detection using Deep Packet Inspection with Hybrid DenseNet121-BiLSTM 1

Automation of IoT Network Traffic Analysis and Anomaly Detec...

引用

1st international conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Geo Francis, E. Sheeja, S. Antony John, E.F. Joseph, Jismy Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Sri Krishna Adithya College of Arts and Science Department of Data Science Coimbatore India Department of Mathematics Thrissur India Department of Mathematics Mala India

ISBN: (纸本)9798331529246

Network security is one of the most crucial problems facing in Internet of Things. An essential component of information systems' long-term viability and regular functioning is network intrusion detection. One potential technique that aids in the identification and defense against network threats is deep packet inspection (DPI). A novel DPI method utilizing a blend of LSTM, GRU, and Recurrent Neural Networks is presented in the paper. The proposed model enhances the accuracy and reliability of detecting outcomes for the threats included in network traffic data. The proposed system detects and identify particular threats, such as malware exploitation, DoS attacks, and unauthorized access attempts, allowing linked devices and services with an accuracy of 99.99%. The system, therefore, can be applied in real-time monitoring systems, intrusion detection and prevention systems, and forensic investigations. Through a series of quantitative trials and comparisons with the most recent formulations, the effectiveness of this approach is assessed. © 2025 ieee.

关键词： Network intrusion

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Cloud Computing based Network Public Opinion Monitoring and Processing Platform 1

Cloud Computing based Network Public Opinion Monitoring and ...

引用

1st international conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Xue, Xiaoqing He, Jianhua Wenhua College Wuhan China Huazhong University of Science and Technology School of Management Wuhan China

ISBN: (纸本)9798331529246

Nowadays, the rapid growth of internet and social media has led to an explosion of online public opinion, making it increasingly important to monitor and analyze the network of public opinion in effective manner. However, the existing public opinion monitoring systems faced difficulties in recognizing the key opinion leaders as well as failed to track the opinion evaluation. To overcome this issue, the Cloud based Public Opinion Monitoring (CPOM) is proposed. Initially, the data is generated from the crawler tool and then preprocessed with stop words removal. The terms with lack of semantic significance in the document are removed in the preprocessing. After that, preprocessed data is further fed into Term Frequency-Inverse Document Frequency (TF-IDF) to convert the data into a set of vectors for better public opinion monitoring. In addition, the filtered data is stored in cloud server for effective management and efficient data storage in order to reduce the need of manual intervention. Finally, the public opinions are monitored by using information analysis and public opinion alarms. From the results, the proposed CPOM attained better results in terms of accuracy (95.6%), precision (93.7%), recall (90.2%) and F1-score (94.2%) when compared to existing Long Short Term Memory (LSTM). © 2025 ieee.

关键词： Cloud platforms

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Recommendation System using Emotion Detection 3

Recommendation System using Emotion Detection

引用

3rd international conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Susindra, S. Sweta, B. Keerthikha Shri, R. Brindha, K. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331527549

The need for a personalized user experience brought recommendation systems to the forefront of digital innovation. However, traditional approaches tend to often forget human emotions, which represent a critical driver in decision making. The paper proposes a new framework in which the detection of emotions becomes part of the recommendation system through advanced facial expression analysis and sentiment evaluation. The paper discusses a hybrid architecture combining emotion recognition via CNN and a collaborative filtering technique for dynamic recommendations. The study outcome demonstrated an enormous improvement in user engagement and satisfaction. It further underlines the transformative potential of emotion-aware systems. © 2025 ieee.

关键词： Emotion Recognition

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Breast Cancer Detection using Domain-Adversarial Training (DANN) with Invariant Risk Minimization (IRM) Hybrid Approach 3

Breast Cancer Detection using Domain-Adversarial Training (D...

引用

3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Akash, K.V. Daniel, Esther Seetha, S. Durga, S. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India Cmr Institute of Technology Department of Information Science & Engineering India Amrita Vishwa Vidyapeetham Tifac Core in Cyber Security Amrita School of Engineering India

ISBN: (纸本)9798331509675

The breast cancer detection performs a key function in the health care network. The precise and early detection of cancer in the breast could aid to save life of the sufferer. The traditional machine learning methods struggle due to the data used to train is different from data used later this is known as Domain shifts. This project uses a hybrid model known as Domain-Adversarial Training of Neural Networks (DANN) with Invariant Risk Minimization (IRM) Hybrid Method to identification of the tumor in the breast. Through integrating the (DANN) and IRM works perfectly among the various type of data and also with the various image from the various sources. This model is more precise and reliable than any other older *** that this model act as an effective tool for detecting the breast cancer in the early stages. © 2025 ieee.

关键词： Adversarial machine learning

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Comprehensive Analysis of Clustering Methods: Focusing on Fuzzy Clustering

Comprehensive Analysis of Clustering Methods: Focusing on Fu...

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2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Salve, Vaishali P. Ghatule, Magan P. Sinhgad College Of Science Dept. of Electronic Science Pune India

ISBN: (纸本)9798331509828

The objective of clustering algorithms is to group or cluster data according to the underlying patterns and commonalities in the data. They are crucial to many aspects of modern life, including social media, healthcare, marketing and e-commerce, and data organization and analysis. There are many clustering methods available, and new ones are always being developed. There are strengths and limitations associated with each algorithm, and there is currently no one method that works for every task. In this study, the researchers analyzed existing clustering algorithms and categorized popular algorithms along five dimensions: fundamental various clustering techniques, fuzzy clustering, the proposed Fuzzy C-Mean (FCM) clustering, algorithms are classified based on fundamental clustering method and data size, and it also offers a comparison of studies comparing Fuzzy C-Means (FCM) clustering with other traditional clustering algorithms. This classification makes it easier for researchers to understand clustering algorithms from different angles and helps in the identification of algorithms that are appropriate for resolving particular problems. Lastly, they discussed the current condition of clustering algorithms now and possible future developments. The researchers additionally discussed and noted the field's open difficulties and unresolved issues. © 2025 ieee.

关键词： Clustering algorithms

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Unlocking Clinical Trial Efficiency and Security with Blockchain and Quantum Technology

Unlocking Clinical Trial Efficiency and Security with Blockc...

引用

2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Yadav, Vanshika Hajarnis, Pratham Minu, R.I. Srm Institute of Science and Technology Computing Technologies Chennai India

ISBN: (纸本)9798331509828

In this ever-evolving field of studies, clinical trials demand efficiency and data security. The project will investigate the integration of blockchain, in combination with quantum technology in solving challenges faced by current clinical trials. This would help in streamlining trial management to guarantee that data is transparent, traceable, and tamper-proof. data tampering, delays in recruitment, and inefficient data systems contribute to slow trials. Most traditional systems lack transparency;hence, data verification can be exhausting, as well as compliance with regulations. The processes involved in patient consent and data sharing are too long. To this end, blockchain provides an immutable ledger relevant for transactions of data with the goal of making the data record transparent and traceable. On the other hand, smart contracts will automate the process of patient consent and the mode of data sharing to minimize the administrative burden to build trust, while quantum technology - or for precision QKD - is to improve the security features of block-chain to face future quantum computer attacks, compromising traditional encryption. Testing and simulation results showed that blockchain integrated with quantum technology will increase information security, reduce costs, accelerate patient recruitment, and speed up data management. Blockchain enables tamper-proof, verifiable entries, while QKD provides strong protection against future cyber threats. This dual-technology approach is, therefore, the solution to the inefficiencies and security vulnerabilities of clinical trials. The project allows further innovations in the pipeline, which quickens the delivery of new treatments to patients with security and efficiency in mind. © 2025 ieee.

关键词： Blockchain

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AI Innovations in Heart Disease Prediction using Machine Learning and Risk Stratification using Deep Learning 14

AI Innovations in Heart Disease Prediction using Machine Lea...

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14th ieee international conference on Communication systems and Network Technologies, CSNT 2025

作者： Rubasri, S. Roshini, M. Hemalatha, S. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331531935

The goal of the heart disease prediction project is to use machine learning and deep learning techniques to create an accurate and efficient system. To obtain insights into the dataset, the project starts with thorough data preprocessing, which includes data cleansing, addressing missing values, and conducting exploratory data analysis. data visualisation tools are used to support this research in order to find important patterns and correlations that help with the model-building *** combining several decision trees, the method increases the predictive potential of the model. To guarantee robustness and dependability, performance is assessed using important measures like accuracy, precision, recall, and cross-validation. A deep learning algorithm is also included to manage data on heart illness that is based on images. Convolutional neural networks (CNNs) use the Lenet architecture for image classification, and pre-processing and data augmentation approaches improve model *** comparison to conventional approaches, this work suggests a sophisticated heart disease prediction model that combines Machine Learning (ML) and Deep Learning (DL) techniques, attaining an accuracy of X% and greatly lowering misclassification rates. By employing oversampling approaches, the suggested system effectively manages unbalanced datasets and enhances prediction robustness through feature selection and ensemble learning. © 2025 ieee.

关键词： Convolutional neural networks

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