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2025 International Conference on Multi-Agent Systems for Collaborative intelligence, ICMSCI 2025

作者： Nagaraj, P. Prabhu, M. Chaithanya Visist, T. Janani, D. Hitesh, S. Saranath, P. Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Krishnankoil Virudhunagar India Indian Institute of Technology Madras Department of Data Science and Artificial Intelligence Chennai600036 India

ISBN: (纸本)9798331509828

The financial markets are inherently volatile, and investors constantly seek ways to mitigate risks while maximizing returns. This work aims to develop a stop-loss hedging strategy for a portfolio of stocks listed on the National Stock Exchange (Nse) to minimize losses and maximize returns. This strategy utilizes real-time market data to adjust stop-load levels dynamically by predicting future stock. This will protect against market downwards. We used long short-term memory (LSTM) neural networks to predict the next stock value. This AI-driven model allows the system to predict the next stock by using real-time market data and adjust the stoploss level accordingly. In addition, this system integrates a hedging strategy. It determines whether to hedge based on the predicted stock price compared to the current price. If the predicted price is less than the current price a hedging strategy like short selling or buying put option is suggested. This automated solution is useful for retail investors. This strategy will protect against losses and returns maximum profits. When the market is upside the stoploss position also increases and will make maximum profits. If the market is downward it will protest from losses by putting a stoploss level and it will return minimum losses. Here we use different algorithms in the work like decision tree, random forest, LSTM, and SVM. Based on the all algorithms we got the highest accuracy for the decision tree and the accuracy is 99.54%. © 2025 IEEE.

关键词： data Analysis data science Decision Tree Economic Growth Hedging Strategy Long Term Short Memory NSE Random Forest Stocks Stop - Loss SVM

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Noise-Filtering Enhanced Deep Cognitive Diagnosis Model for LatentSkill Discovering

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Intelligent Automation & Soft Computing 2023年第8期37卷 1311-1324页

作者： Jing Geng Huali Yang Shengze Hu National Engineering Research Center of Educational Big Data Central China Normal UniversityWuhan430079China Faculty of Artificial Intelligence in Education Central China Normal UniversityWuhan430079China School of Computer Science and Artificial Intelligence Wuhan Textile UniversityWuhan430079China

Educational data mining based on student cognitive diagnosis analysis can provide an important decision basis for personalized learning tutoring of students,which has attracted extensive attention from scholars at home and abroad and has made a series of important research *** this end,we propose a noise-filtering enhanced deep cognitive diagno-sis method to improve the fitting ability of traditional models and obtain students’skill mastery status by mining the interaction between students and problems nonlinearly through neural ***,modeling complex interactions between students and problems with multidimensional features based on cognitive processing theory can enhance the interpretability of the proposed model;second,the neural network is used to predict students’learning performance,diagnose students’skill mastery and provide immediate feedback;finally,by comparing the proposed model with several baseline models,extensive experimental results on real data sets demonstrate that the proposed Finally,by comparing the proposed model with several baseline models,the extensive experimental results on the actual data set demon-strate that the proposed model not only improves the accuracy of predicting students’learning performance but also enhances the interpretability of the neurocognitive diagnostic model.

关键词： Cognitive diagnosis nonlinear interaction interpretability intelligent education system skill diagnosis

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data Reduction Algorithm: Identification and Elimination of Erroneous rows

Data Reduction Algorithm: Identification and Elimination of ...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, artificial intelligence and Telecommunication engineering, RMKMATE 2023

作者： Katale, Tejaswini Mahalle, Parikshit N. Gautam, Waidehi Shinde, Gitanjali R. Computer Engineering Vishwakarma Institute of Technology Pune India Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9798350305708

data Reduction without the removal of exact, correct rows is a crucial pre-processing step. Large datasets make it difficult to model data effectively or forecast results accurately. Additionally, they demand lengthy processing times, sophisticated complexity software and thorough data cleaning. The incorrect and irrelevant rows may produce inaccurate results that impair the performance of the model. For better and more accurate outcomes it is crucial to properly detect and remove inaccurate data. The proposed algorithm calculates the Initial Recall value of the dataset. It eliminates least correlated features using the Correlation Matrix. Using Gaussian Curve, for all the columns it identifies and eliminates rows having values which lie beyond (µ ± 3σ). Furthermore, it takes into account the column with the highest Standard Deviation, selects the nearest 50% Left and 50% Right values from that column's mean. It selects only those rows and calculates the Final Recall value. Negligible difference between Initial and Final Recall values implies that the removed rows had no or minimal impact on the dataset's final result. This algorithm is implemented on 3 Standard Medical datasets - Pima Diabetes, Heart Attack and Breast Cancer. For the Breast Cancer dataset, this algorithm eliminated the highest number of rows that is 231. © 2023 IEEE.

关键词： Correlation Matrix data Reduction Gaussian Curve Recall Standard Deviation

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Feature Selection for Classification on High- Dimensional data Using Swarm Optimization Algorithms 7

Feature Selection for Classification on High- Dimensional Da...

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7th IEEE International Conference on Computational Systems and Information technology for Sustainable Solutions, CSITSS 2023

作者： Pradeep Kumar, D. Bj, Sowmya Kanavalli, Anitha Deeptashree, D. Ramaiah Institute of Technology Department of Computer Science and Engineering Bangalore India Ramaiah Institute of Technology Department of Artificial Intelligence and Data Science Bangalore India

ISBN: (纸本)9798350343144

Feature selection plays a crucial role in classification by identifying relevant features. Bio-inspired algorithms, such as genetic algorithms, particle swarm optimization, and ant colony optimization, have gained popularity due to their ability to mimic natural processes and explore high-dimensional feature spaces efficiently. The analysis includes evaluating classification accuracy, and computational efficiency as performance metrics. The experimental setup involves dataset selection, choosing a classification algorithm, implementing bio-inspired feature selection, evaluation, parameter tuning, and comparative analysis. The findings provide valuable insights into the effectiveness and efficiency of bio-inspired algorithms for feature selection in classification. The project focuses on reducing high- dimensional datasets to low-dimensional ones using feature selection through classification with swarm optimization algorithms. It aims to validate the effectiveness of the Jellyfish Search algorithm (JSA), Horse Herd Optimization algorithm (HOA), and Binary Bat algorithm (BBA) on real-world datasets. The objectives include evaluating the algorithms' performance, conducting a comprehensive performance analysis, execution of classifiers like RF, DT, KNN, Voting Classifier and SVM, obtaining and tuning the results, and publishing the findings. The research outcomes contribute to the knowledge of feature selection for classification and provide guidance in selecting appropriate algorithms for specific classification tasks. © 2023 IEEE.

关键词： Classification (of information)

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I/O Steganography for Audio and Images in Secure data Transmission 3

I/O Steganography for Audio and Images in Secure Data Transm...

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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Jebakumar Immanuel, D. Prakash, A. Mugundhan, M. Keerthivasan, U. Uma Makesan, A. Karpagam Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India Sns College of Engineering Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350322842

There are several uses for information concealing today. The use of data hiding knowledge may be morally or immorally acceptable. data hiding methods, however, are difficult to classify into either the steganography or the watermarking categories because there is no clear distinction between these two concepts, and classification mostly depends on algorithmic application. Hence, regardless of how data concealing is classified, the most popular data hiding applications include copyright protection, covert communication, safe storage, and fingerprinting. Messages are altered by cryptography so they can't be understood. Secured data, varied size keys for data concealment, and quick and flexible ease of design are some of cryptography's main benefits. The biggest drawback of cryptography is that it is not suitable for the internet and is only compatible with devices like mobile phones, has complicated hardware, and uses unusual cypher patterns. Steganography is a centuries-old technique for obscuring data. In this essay, a survey of current audio steganography research is presented. © 2023 IEEE.

关键词： Mean square error

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Enhancing Public Safety with AI-Powered Intelligent Surveillance: An Examination of Immediate Incident Detection and Rapid Response in Urban Settings

Enhancing Public Safety with AI-Powered Intelligent Surveill...

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2023 International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2023

作者： Amingad, S. Chakradhar Rahul, Morathoti Sathvik Reddy, P.S. Gupta, N Sujata Vardhaman College of Engineering Artificial Intelligence and Data Science Hyderabad India Vardhaman College of Engineering Department of Information Technology Hyderabad India

ISBN: (纸本)9798350344387

This study describes a comprehensive smart surveillance system that integrates algorithms for face recognition, facial expression detection, and behavioral analysis. The technology improves on traditional surveillance by offering real-time insights into individual characteristics and activities. Deep learning, especially convolutional neural networks (CNNs), is used for robust face identification even under difficult settings. Furthermore, it uses machine learning, such as deep neural networks, to classify emotional expressions, providing insights into people's moods. The behavioral analysis module of the system detects suspicious activities and abnormalities using computer vision methods such as optical flow and action recognition models that have been trained on a large dataset of annotated behaviors. These components work together to form a single framework for real-time monitoring and analysis of persons in the surveillance region. The system excels in properly recognizing faces, recognizing emotions, and detecting suspicious activities, resulting in increased security and efficiency in surveillance applications. © 2023 IEEE.

关键词： convolutional neural network (CNN) Proactive Monitoring Sensor technology Smart surveillance system Urban safety

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Personality Prediction Using Machine Learning 1

Personality Prediction Using Machine Learning

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1st International Conference on Computational intelligence for Information, Security and Communication Applications, CIISCA 2023

作者： Pooja, K. Rakshitha, G.H. Spoorthi, S. Kamal, Shoaib Rao, Trupthi Ashwini, K. Global Academy of Technology Department of Computer Science and Engineering Bangalore India Global Academy of Technology Department of Artificial Intelligence and Data Science Bangalore India

ISBN: (纸本)9798350339727

With the growth of social networks, a wide range of methodologies have been developed to describe users' personalities only based on their language and social media use habits. Persona prediction is very popular these days. It examines consumer behaviour and records the user's ideas, emotions, and so on. There has been a sufficient amount of research in this field over the years. This survey provides an overview of the numerous methods tried to predict personality and behaviour from the use of the social media content. The capacity to anticipate the personality attributes of users can help create a variety of specialised goods or services. The concluding phase then provides the future characteristics and directives. © 2023 IEEE.

关键词： Machine learning

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An IoT-based Bearded Dragon Enclosure Integrating YOLO Object Detection for Monitoring Food and Cleanliness 12

An IoT-based Bearded Dragon Enclosure Integrating YOLO Objec...

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12th IEEE Global Conference on Consumer Electronics, GCCE 2023

作者： Varisthanist, Nanmanat Tardtong, Thanapat Jadee, Kittitach Srijiranon, Krittakom Thammasat University Faculty of Science and Technology Thammasat University Research Unit in Data Innovation and Artificial Intelligence Department of Computer Science Pathumthani Thailand

ISBN: (纸本)9798350340181

Among exotic reptiles, bearded dragons (Pogona) are popular pets. As ectotherms, bearded dragons require special food and controlled environments, including temperature, humidity, ultraviolet light, and cleanliness. This research applied the Internet of Things (IoT) to control environments within the pet enclosure. In addition, the You Only Look Once (YOLO) deep learning algorithm applied to convolutional neural networks was used to create object detection to count crickets eaten by a dragon and detect feces inside the enclosure. To find the appropriate YOLO structure, three varied experiments were proposed, including different versions YOLO and sizes as well as hyperparameter tuning. Results were that YOLYv5s with 16 batches and 0.01 learning rate was the appropriate structure with an average precision of 0.8246 and 0.6051 for detecting crickets and feces, respectively, at a mean average precision of 0.7149. The proposed enclosure with object detection may improve efficiency and reduce redundancy in breeder tasks. © 2023 IEEE.

关键词： Enclosures

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Multi-features Enhanced Multi-task Learning for Vietnamese Treebank Conversion 23

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23rd Chinese National Conference on Computational Linguistics, CCL 2024

作者： Zhang, Zhenguo Liu, Jianjian Li, Ying Yunnan Key Laboratory of Artificial Intelligence Kunming China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China

ISBN: (纸本)9780000000002

Pre-trained language representation-based dependency parsing models have achieved obvious improvements in rich-resource languages. However, these model performances depend on the quality and scale of training data significantly. Compared with Chinese and English, the scale of Vietnamese Dependency treebank is scarcity. Considering human annotation is labor-intensive and time-consuming, we propose a multi-features enhanced multi-task learning framework to convert all heterogeneous Vietnamese Treebanks to a unified one. On the one hand, we exploit Tree BiLSTM and pattern embedding to extract global and local dependency tree features from the source Treebank. On the other hand, we propose to integrate these features into a multi-task learning framework to use the source dependency parsing to assist the conversion processing. Experiments on the benchmark datasets show that our proposed model can effectively convert heterogeneous treebanks, thus further improving the Vietnamese dependency parsing accuracy by about 7.12 points in LAS. © 2024 China National Conference on Computational Linguistics.

关键词： Multi-task learning

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Chinese Grammatical Error Correction via Large Language Model Guided Optimization Training 23

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23rd Chinese National Conference on Computational Linguistics, CCL 2024

作者： Liu, Xiao Li, Ying Yu, Zhengtao Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Yunnan Key Laboratory of Artificial Intelligence Kunming China

ISBN: (纸本)9780000000002

Pre-trained language model-based methods for Chinese Grammatical Error Correction (CGEC) are categorized into Seq2Seq and Seq2Edit types. However, both Seq2Seq and Seq2Edit models depend on high-quality training data significantly. Considering the strong generation and inference ability of large language models (LLMs), we propose a large language model-guided optimization training method to exploit LLMs to extract error knowledge to optimize the traditional CGEC model training process. On the one hand, we use error types and confusion sets as extra knowledge to guide LLMs to generate diverse pseudo data, thus extending the error distribution of our training data. On the other hand, LLMs are utilized to infer the predicted results from our CGEC models and obtain the re-training data, thus iteratively optimizing our pre-trained CGEC models. Experiments on two benchmark datasets show that our LLMs-guided optimization method with small-scale training data can achieve comparable results with baseline models with large-scale training data. Detailed comparison experiments demonstrate that both the early deviser pseudo data and the later re-training data are extremely useful for traditional CGEC model optimization training, and can benefit from each other. We will release our code and prompts at https://***/SakuraAcedia/llm-cgec-got to facilitate future work. © 2024 China National Conference on Computational Linguistics.

关键词： Digital elevation model

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