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Dynamic Landslide Prediction, Monitoring, and Early Warning with Explainable AI: A Comprehensive Approach 3

Dynamic Landslide Prediction, Monitoring, and Early Warning ...

引用

3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Binu, Kristin Elizabeth Anoopkumar, Lakshmi Thampi Sunil, Maria Jose, Merin Preetha, K.G. Rajagiri School of Engineering & Technology Dept. of Computer Science and Engineering Rajagiri Valley PO Kochi India

ISBN: (纸本)9798350375190

Landslides are a major threat to infrastructure, human lives, and the environment. Therefore, early warning systems and precise forecasting are essential for reducing their effects. This study investigates how to enhance landslide prediction models using Explainable Artificial Intelligence (XAI) methodologies. Intending to improve complex machine learning models' interpretability and transparency, XAI seeks to improve comprehension and confidence in the model's predictions. XAI methodologies used are SHapley Additive exPlanations (SHAP), LIME (Local Interpretable Model-agnostic Explanations), and feature importance analysis. These techniques produce comprehensible explanations for each forecast and assist in determining the most significant variables in landslide prediction. The proposed XAI-enhanced landslide prediction model demonstrates promising results in terms of accuracy and interpretability. The research reported here helps to design more potent early warning systems for landslides by advancing explainable AI applications in geoscience and disaster risk reduction. © 2024 IEEE.

关键词： Landslides

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Deepfake detection for preventing Audio and Video frauds using Advanced Deep Learning Techniques

Deepfake detection for preventing Audio and Video frauds usi...

引用

2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Bharadwaj, Rakhi Ratnaparkhi, Soham Rajpurohit, Rajendrasingh Rahate, Kashish Pandita, Rahul Thosar, Samarth Vishwakarma Institute of Technology Dept. of Computer Engineering Pune India

ISBN: (纸本)9798350315455

Deepfakes, powered by generative adversarial networks (GANs), automate the creation of deceptive videos. To combat this menace, the study assembles an advanced model that fuses ResNeXt, Long Short-Term Memory (LSTM), and ResNet architectures, renowned for their efficacy in handling visual and temporal aspects, for the detection of deepfakes in audio and video content. Pre-processing, facilitated by a Multi-Task Cascaded-Convolutional Neural Network (MTCNN), accurately extracts facial regions. The model undergoes rigorous evaluation across three key datasets which are, FaceForensics++(FF-DF), Celeb-DF, and Facebook Deepfake Detection Challenge (DFDC), affirming its real-world readiness and exceptional accuracy. The combined models consistently deliver highly precise results, maintaining their robustness against evolving deepfake technologies. © 2023 IEEE.

关键词： Long short-term memory

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AI Approach for Minimizing the Energy Consumption of Servers Using Deep-Q-Learning 8

AI Approach for Minimizing the Energy Consumption of Servers...

引用

8th IEEE International Conference for Convergence in technology, I2CT 2023

作者： Kaulage, Anant Shaha, Shraddha Naik, Tanaya Nikumbh, Khushi Jagtap, Vedant Vishwakarma Institute of Technology Dept. of Computer Engineering Pune India

ISBN: (纸本)9798350334012

This paper focuses on minimizing energy consumption by servers in data centers. Server's energy consumption can be impacted by numerous factors, such as the number of connected devices, the workload being processed, and the energy efficiency of the *** energy consumption can be serious because of several reasons as it can impact the reliability of servers because high temperatures generated by energy consumption can lead to hardware failure and other technical issues. Therefore, reducing energy consumption in servers is important for improving the cost-effectiveness, sustainability, scalability, and reliability of data center operations. A type of reinforcement learning called Deep Q-Learning (DQL) can be used to address issues with server energy consumption. The basic idea behind DQL is to train an artificial agent, such as a neural network, to make decisions about energy consumption in real time. The agent is trained by frequently performing actions in a setting and earning rewards depending on the amount of energy consumed by specific actions. Over time, the agent learns which actions are most likely to lead to energy savings, and it can then be deployed to make real-time decisions about energy consumption in a server. Experimental results of the proposed research show an average of 66% power saving in the server's consumption of energy using Deep Q-Learning (DQL). © 2023 IEEE.

关键词： Energy utilization

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BSCIAM: A Blockchain based Secure Cloud Identity and Access Management Framework

BSCIAM: A Blockchain based Secure Cloud Identity and Access ...

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2024 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2024

作者： Das, Swatisipra Sahil, Mohammad Pandit, Naveen Kumar Priyadarshini, Rojalina Gochhayat, Sarada Prasad Fakir Mohan University Postgraduate Dept. of Computer Science Odisha India C.V. Raman Global University Dept. of Computer Science and Engineering Odisha India Indian Institute of Technology Jammu Dept. of Computer Science and Engineering J&K India

ISBN: (纸本)9798350348460

Cloud computing enables on-demand computation on remote servers and computers. Thanks to the adaptability and scalability of the infrastructure for data storage, processing, and management. To solve the security problems arising in cloud identity management techniques such as, dependency on a third-party token provider, leakage of user's details, single point of failure, etc., decentralized cloud identity management systems came into the picture. Blockchain is a decentralized database providing immutability and transparency to recorded transaction data. The current token-based decentralized cloud identity management systems have limitations, including the absence of data access management procedures and a lack of security features. This paper suggests a Blockchain based Secure Cloud Identity and Access Management (BSCIAM) model for secure identity and access management, utilizing encryption and key sharing techniques. The proposed model has been implemented using Ethereum smart contracts for token-based identity management. © 2024 IEEE.

关键词： access management blockchain cloud identity management

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ML based Text Summarization for Sentiment Analysis in Information Retrieval using Feature engineering 4

ML based Text Summarization for Sentiment Analysis in Inform...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Shirley, C.P. Berin Jeba Jingle, I. Venkatesan, R. Tamilarasan, R. Yashvanth Ram, R.V. Joyce Beryl Princess, P. Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Dept. of Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Dept. of Ai & Ml Coimbatore India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540364

Sentiment analysis plays a crucial role in understanding user preferences and opinions in the field of information retrieval. This research focuses on developing a system that combines text summarization and machine learning techniques to improve sentiment analysis accuracy. The proposed approach employs extractive and abstractive summarization techniques to reduce lengthy texts into concise summaries while preserving key information. Feature engineering is then applied to extract sentiment-related elements from the summary text, providing valuable insights for the SVM model. The SVM model, enhanced with feature engineering, is expected to effectively summarize text and accurately detect sentiments expressed in documents. This research contributes to the advancement of sentiment analysis techniques and provides valuable insights for decision-making and content development. © 2024 IEEE.

关键词： Support vector machines Measurement Sentiment analysis Accuracy Text summarization Machine learning Information retrieval Feature extraction Vectors Resilience

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Land Use Land Cover Classification Using Machine Learning and Remote Sensing Data: A Case Study of Karnaprayag, Uttarakhand, India 1

Land Use Land Cover Classification Using Machine Learning an...

引用

1st International Conference on Electronics, Communication and Signal Processing, ICECSP 2024

作者： Saini, Rashmi Singh, Suraj Govind Ballabh Pant Institute of Engineering and Technology Dept. of Computer Science and Engineering Pauri Garhwal India

ISBN: (纸本)9798350364590

Land use and land cover (LULC) classification is crucial for planning and sustainable development, especially in mountainous regions. This study focuses on the Tehsil Karnaprayag, situated in the vicinity of the Greater Himalaya. With a total area of 1858.903 km2 and elevations ranging from 647m to 7641m. Using multispectral Sentinel-2 imagery and Machine Learning techniques i.e. Extreme gradient boosting (XGBoost) and K-nearest neighbor (KNN). This study aims to classify seven distinct land cover classes: snow, forest, water bodies, grassland, agricultural land, fallow land, and built-up areas. The classification results demonstrate the effectiveness of XGBoost classifier achieving an accuracy of 90.22% and a kappa value of 0.882, while KNN achieved an accuracy of 88.59% with a kappa value of 0.862. The analysis reveals that forest and fallow land are the predominant land cover types in the study area, constituting nearly 60% and 15% of the total area respectively. Notably, XGBoost exhibits the highest class-specific accuracy of 97.60% for the snow class, demonstrating its utility in discriminating between challenging land cover types. This research highlights the importance of LULC classification in informing land management strategies and facilitating sustainable development initiatives in mountainous regions. The findings provide valuable insights into the spatial distribution and composition of land cover in Tehsil Karnaprayag, essential for effective planning and conservation efforts in the Himalayan ecosystem. © 2024 IEEE.

关键词： Sustainable development

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Next Generation Social Media Platform to Move from Centralization to Decentralization of Data 7

Next Generation Social Media Platform to Move from Centraliz...

引用

2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Dhore, M.L. Ratnaparkhi, Soham Sasne, Rohan Surase, Om Chandak, Rushikesh Vishwakarma Institute of Technology Dept. of Computer Engineering Pune India

ISBN: (纸本)9798350304268

Social media has revolutionized global communication, business contracts, and the diffusion of ideas. However, there are several potential drawbacks to this, including the spread of false information, data breaches, identity fraud, effective content monitoring, and digital piracy. Using blockchain protocols and Web3 philosophies, it is possible to protect data privacy, data security, and fraudulent identities. By replacing traditional email/name-password authentication with Metamask authentication, user privacy is strengthened. Storing application media on the Ethereum blockchain, a proof-of-concept for application data can be established, thereby preventing data breaches. According to our research results, blockchain technology has the potential to provide tamper resistance, traceability, and transparency. As a result, this proposed research provides a social media platform that is truly decentralized and built on a network, enhancing its users' privacy. © 2023 IEEE.

关键词： blockchain decentralized network identity fraud Social media Web3

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Detection and Classification of Aggressive Comments and Hate Speech

Detection and Classification of Aggressive Comments and Hate...

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2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Nemade, Srushti Mane, Sunil B. Nandgaonkar, Sushma Dept. of Computer Engineering and Information Technology Maharashtra Pune India

ISBN: (纸本)9798350323917

The prevalence of social media has led to an increase in aggressive online behaviour such as cyberbullying, trolling, hate speech, and flaming. The anonymity and convenience of social media platforms have allowed individuals to express their aggressive tendencies without fear of retaliation. This issue is not limited to certain sections of society and has impacted people globally. Cyberbullies now have the power to affect the lives of millions of people worldwide, which can be detrimental to the internet's importance as a communication and networking tool. Therefore, it is critical to recognize and restrict this negative behaviour. It is essential to detect and prevent cyberbullying and hate speech in Indian native languages, considering India's diverse population and multiple languages. Social media platforms have brought about significant benefits, including improved communication and networking, but the increased use of these platforms has led to an increase in harmful behaviour that can negatively impact mental health. Additionally, misinformation and propaganda can cause severe consequences for society. This paper discusses the detection and classification of aggressive comments in Hindi-English code-mixed and Hindi data from popular social media platforms such as Facebook, Twitter, and Instagram. © 2023 IEEE.

关键词： Social networking (online)

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SmartGuard: Support Vector Machine(SVM)-Powered Defense Mechanism for Phishing Prevention

SmartGuard: Support Vector Machine(SVM)-Powered Defense Mech...

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Rambharat, Yadav Abhishek Shelke, Dev Ritesh Khaleel, Kundlik Adnan Arunkumar, Anande Aditya Ekhande, Deepa Pillai College of Engineering Dept. Computer Engineering New Panvel India Pillai College of Engineering Dept. Information Technology New Panvel India

ISBN: (纸本)9798331517953

SmartGuard serves as a strong defense system against data breaches and financial risks from hackers by utilizing a range of machine learning algorithms, including Support Vector Machine (SVM). The proposed system involves a website that accepts URLs as input from users and employs various models to predict the legitimacy of the provided URL, distinguishing between legitimate and malicious websites. Additionally, users can input messages containing links for further analysis. Nowadays, many phishing links are received by people on WhatsApp and Emails. These links pretend to be genuine but in reality, are malicious. Clicking on this link can cause data or monetary loss to an individual. The proposed system also incorporates a client-side extension, and leverages many parameters to evaluate the authenticity of a website, protecting users from visiting potentially malicious sites. The integration of AI technologies and the user friendly interface make SmartGuard a robust tool against Phishing and other cyberattacks. © 2024 IEEE.

关键词： Phishing

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Lane Change Detection with an Ensemble of Image-based and Video-based Deep Learning Models

Lane Change Detection with an Ensemble of Image-based and Vi...

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2023 Innovations in Intelligent Systems and Applications Conference, ASYU 2023

作者： Nalcakan, Yagiz Bastanlar, Yalin İzmir Institute of Technology Computer Engineering Dept. İzmir Turkey

ISBN: (纸本)9798350306590

Prediction of lane-changing maneuvers of surrounding vehicles is important for autonomous vehicles to understand the scene properly. This research proposes a vision-based technique that only requires a single in-car RGB camera. The surrounding vehicles' maneuvers are classified as right/left lane-change or no lane change conforming to most lane change detection studies in the literature. The usual practice in previous studies is feeding individual video frames into CNN to extract features and afterward using an LSTM to classify the sequence of features. Differently, in our study, we exploit the power of ensembling the prediction results of two methods. The first one uses a small feature vector containing the image coordinates of the target vehicle and classifies it with an LSTM. The second method works with a simplified scene representation video (only the target vehicle and ego-lane highlighted) and it is based on a self-supervised contrastive video representation learning scheme. Since maneuver labeling is not required in the self-supervised learning step this enables the use of a relatively large dataset. After the self-supervised training, the model is fine-tuned with a labeled dataset. Our experimental study on a well-known lane change detection dataset reveals that both of the mentioned methods by themselves achieve state-of-the-art results and ensembling them increases the classification accuracy even more. © 2023 IEEE.

关键词： Autonomous vehicles

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