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Prediction of Suicidal Behaviour among the users on Social Media using NLP and ML 4

Prediction of Suicidal Behaviour among the users on Social M...

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4th international conference on Emerging systems and intelligent computing, ESIC 2024

作者： Mishra, Tapas Kumar Sucharitha, G. Siddhartha, Narala Raju, Bommena Mohanty, Sachi Nandan Srm University-Ap Dept of Computer Science and Engineering Andhra Pradesh 522240 India Institute of Aeronautical Engineering Dept of Electronics and Communication Engineering Telangana India Telangana India VIT-AP University School of Computer Science and Engineering Andhra Pradesh Amaravati India

ISBN: (纸本)9798350349856

suicide attempts. Our findings contribute to the growing field of suicide prevention through cutting-edge machine learning techniques applied to natural language data. According to our experimental results, the PM model outperformed other machine learning algorithms with the highest classification score. It achieved an impressive accuracy of 92% and an F1 score of 85%. These findings highlight the effectiveness and potential of the PM model for the task at hand. Suicides are happening more often these days. Social media messages and chats are the preferred means of expression for those who are committing suicide. Several studies have shown that it is possible to spot suicide suspects from their online conversations and posts on social media. Consequently, it is imperative to create a machine learning system for automatic early identification of suicide ideation or any abrupt changes in a user's behaviour by examining his or her posts and chats on social media such as Twitter, Instagram, WhatsApp, and Facebook. Our research is focused on significantly improving the performance of our model to accurately recognize early indications of suicide attempts with high precision, thus contributing to the prevention of such tragic events. To achieve this, we utilized advanced text pre-processing techniques and feature extraction approaches, including CountVectorizer and word embedding. Our study involved training both XGBoost and NLP models, which were rigorously evaluated using a substantial dataset of 34,100 samples. Furthermore, to assess real-world applicability, we conducted live tests of our model using tweets collected via the Stream lit python-based web interface tool. The results of our experiments are promising, demonstrating the potential of our approach in detecting early signs of distress and aiding in the timely intervention to prevent. © 2024 ieee.

关键词： Natural language processing systems

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An Enhanced Speech Emotion Recognition System using RNN compared with CNN

An Enhanced Speech Emotion Recognition System using RNN comp...

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2025 international conference on intelligent computing and Control systems, ICICCS 2025

作者： Jambukumar, S. Devi, P.Meenakshi Saravanan, N. Divakar, S. Tharnesh, R. Indiresan, K. K S R College of Engineering Department of Information Technology Tiruchengode 637215 India K S R Institute for Engineering and Technology Department of Information Technology Tiruchengode 637215 India

ISBN: (纸本)9798331512088

The aim of the work is to design a secure and accurate Speech Emotion Recognition (SER) system using a RNN model to classify emotions from speech data. The system's predictive accuracy is evaluated and compared with the performance of a standalone CNN-based model. The suggested RNN model achieves an accuracy of 96.96%, significantly better than the CNN model's accuracy of 93.93%. Group 1 has a CNN-based Speech Emotion Recognition system that was evaluated using ten models of different accuracy. Group 2 includes an advanced RNN model with convolutional and recurrent layers to maximize predicting accuracy. The CNN-based model achieves an accuracy range of 90.20% to 93.93%, while the RNN model demonstrates superior performance with an accuracy range of 93.80% to 96.96% under similar conditions. Testing parameters include a maximum difference of 2.50 and a minimum of 1.20, with a step size of 0.10 and a confidence level of p < 0.05. In this work, it is seen that the RNN model has significantly higher accuracy than the CNN model. © 2025 ieee.

关键词： Accuracy CNN data Emotion recognition Prediction RAVDESS RNN Speech recognition

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Design of an intelligent Asset Recovery System Based on Deep Learning 4

Design of an Intelligent Asset Recovery System Based on Deep...

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4th international conference on Electronic Information Engineering and Computer Technology, EIECT 2024

作者： Yang, Fang Guizhou University Faculty of Public Administration Guizhou Guiyang550001 China

ISBN: (纸本)9798331528850

The increasing complexity of financial crimes poses significant challenges for traditional asset recovery methods, which often fail to effectively trace covert fund transfers and complex money laundering schemes. To address these limitations, this paper proposes and implements an intelligent asset recovery system based on deep learning, incorporating advanced computational techniques to enhance the detection and analysis of complex financial transactions. The system utilizes a hybrid CNN-LSTM model to extract both spatial and temporal features from transaction data, while a self-attention mechanism dynamically highlights critical transaction patterns, improving the model's interpretability and recognition capabilities. A multimodal data fusion approach integrates diverse sources, including bank transaction records, social network data, and public financial statements, enabling comprehensive analysis of heterogeneous data. Experimental results demonstrate that the proposed system significantly outperforms traditional methods in key metrics, achieving higher accuracy, precision, recall, and Fl scores. Moreover, it exhibits superior performance in identifying intricate fund transfer chains and adapting to diverse financial crime scenarios. The study not only provides a robust, automated tool for asset recovery but also establishes a foundation for the development of intelligent systems aimed at preventing and combating financial crimes. Future research will focus on enhancing system scalability, optimizing computational efficiency, and integrating additional data sources to further expand its application scope. © 2024 ieee.

关键词： Decentralized finance

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advanced Techniques in data Ingestion and Pipelining for Scalable Big data Platforms: A Comprehensive Review 4

Advanced Techniques in Data Ingestion and Pipelining for Sca...

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4th ieee international conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Murarka, Sachin Jain, Anshuj Singh, Laxmi Rabindranath Tagore University Department of Electronics & Communication Engineering Bhopal India Scope Global University Department of Electronics & Communication Engineering Bhopal India

ISBN: (纸本)9798331518981

In the era of big data, effective data ingestion and pipelining are critical for organizations to harness the power of vast and diverse data sources, such as IoT devices, social media, and transactional systems. data ingestion refers to the process of systematically collecting and importing data for processing, while data pipelining ensures the continuous flow of this data through various transformation stages, preparing it for analysis. As data volumes grow, platforms like Hadoop, with its HDFS (Hadoop Distributed File System) and MapReduce, have become essential in managing large-scale datasets by providing distributed storage and parallel processing capabilities. Additionally, tools such as Apache Kafka and Apache Flume facilitate both batch and real-time data ingestion, ensuring flexibility in handling different types of data streams. However, challenges remain, especially with the increasing complexity of datasets and the growing need for real-time processing. Emerging technologies such as cloud-native ingestion frameworks, edge computing, and AI-enhanced pipelines offer solutions to these challenges by improving scalability, performance, and cost efficiency. Machine learning is also playing an evolving role in optimizing pipeline workflows, enabling more intelligent and adaptive data processing. This paper reviews key technologies for data ingestion and pipelining, discusses their limitations, and explores cutting-edge trends aimed at enhancing the efficiency of big data platforms. These advancements promise to reshape how organizations handle big data, offering improved agility and operational effectiveness. In addition to established technologies, the rise of cloud-native architectures and AI-driven solutions is revolutionizing data ingestion and pipelining. Cloud platforms offer scalable, on-demand resources, while AI and machine learning enhance automation, anomaly detection, and predictive analytics within pipelines. © 2024 ieee.

关键词： Cloud platforms

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Autism Detection System using Behavioral data 1

Autism Detection System using Behavioral Data

引用

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

作者： Vijaya Shetty, S. Sowmya, P. Srujana, S.N. Shruthi Shetty, J. Mishra, Aditya Zaid, Mohd Nitte Meenakshi Institute of Technology Department of Cse Bengaluru India

ISBN: (纸本)9798331529246

This study introduces an automated autism spectrum disorder (ASD) detection system employing advanced machine learning techniques. The system integrates behavioral and physiological data from diverse sources. The score parameters of various machine learning models, such as Random Forest, Decision Tree, SVM, and AdaBoost, are compared. Pickling is performed on each model for easy storage and retrieval, and their predictions are aggregated to present a majority decision. Extensive experiments are conducted on a diverse dataset comprising toddlers, children, and adults to assess the system's performance in accurately classifying individuals as ASD or non-ASD. The system aggregates predictions from various models using a majority voting algorithm to present a majority decision, ensuring reliable and efficient early ASD identification across all age groups. The proposed system achieved an accuracy of 97%, with the majority voting algorithm enhancing reliability and efficiency in early ASD identification across all age groups. The novelty of this work lies in integrating behavioral and physiological data from diverse sources, along with the majority voting algorithm, to develop an automated ASD detection system using machine learning techniques. © 2025 ieee.

关键词： Adversarial machine learning

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A Feature Selection Method Based on Hybrid Natural Inspired Algorithms 11

A Feature Selection Method Based on Hybrid Natural Inspired ...

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11th ieee international conference on intelligent data acquisition and advanced computing systems - Technology and Applications (IDAACS)

作者： Xia, Xiaoyu Ye, Zhiwei Sun, Shuang Hubei Univ Technol Sch Comp Sci Wuhan Peoples R China

ISBN: (纸本)9781665426053

data mining is one of the fastest-growing research domains in the information industry as a result of the wide availability of numerous data. Feature selection is the important preprocessing step to affect the performance of data mining. Ttimum. Besides that, hybrid algorithms maintain the advantages of each single-mode algorithm and avoid its weakness effectively. In this work, we propose a parallel model for feature selection named THDWL, which is a combination of Differential Evolution (DE), Whale Ohe natural-inspired algorithm is one of the most effective methods for feature selection, but there are some limitations in the single-mode algorithm such as slow efficiency and local optimization Algorithm (WOA), and Lightning Attachment Procedure Optimization (LAPO). In THDWL, the operators of these algorithms are independently implemented on their subpopulations and communicate at the end of each iteration to get the global best solution. Comparisons are conducted between single-mode algorithms and THDWL to verify the performance of the proposed method. The simulation results show that THDWL effectively improves the classification accuracy and convergence speed comparing with the single-mode algorithm.

关键词： feature selection natural-inspired algorithm hybrid algorithm data mining

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advanced CNN Architecture for Multiclass Brain Tumor Classification

Advanced CNN Architecture for Multiclass Brain Tumor Classif...

引用

2025 international conference on intelligent computing and Control systems, ICICCS 2025

作者： JeyaPriya, D. Avinash, U. Dinesh, V. St. Joseph's College of Engineering Department of Information Technology Chennai India

ISBN: (纸本)9798331512088

Brain tumors are a serious health threat, and correct diagnosis is critical to the success of medical intervention. This research suggests a Convolutional Neural Network (CNN)-based approach to automatically classify MRI brain tumor images into four classes: Glioma, Meningioma, No Tumor, and Pituitary. The goal is to maximize diagnostic accuracy, allowing for early detection and better patient outcomes. A dataset of 7,023 MRI images was used, and augmentation techniques were performed to enhance the model generalization. The CNN architecture was fine-tuned with hyperparameter tuning to get consistent classification performance. The end model achieved an average test accuracy of 99.4% and class-wise precision of 99.6% for Glioma, 99.5% for Meningioma, and 99.5% for Pituitary tumors. These findings show the promise of deep learning in medical diagnosis, providing a trustworthy and efficient automatic brain tumor detection and classification tool. This study supports the development of AI-based healthcare solutions, helping radiologists diagnose more quickly and accurately. © 2025 ieee.

关键词： Brain tumor classification CNN data Augmentation glioma meningioma MRI pituitary TensorFlow

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Model-Free H∞Optimal Control of Multi-Agent systems with Intermittent Exploration Noise via Adaptive Dynamic Programming

Model-Free H∞Optimal Control of Multi-Agent Systems with In...

引用

2024 international Annual conference on Complex systems and intelligent Science, CSIS-IAC 2024

作者： Hu, Shunwei Luo, Yanhong Zhang, Juan Tang, Fanghua College of Information Science and Engineering Northeastern University Shenyang China

ISBN: (纸本)9798331504755

In this paper, the H∞optimal control problem for linear multi-agent systems is studied, and a hierarchical control scheme is designed to reduce the computational complexity. By introducing an additional compensating input indicator, the expected performance indicators are met. The designed output feedback control protocol introduces relaxation parameters, which relax the design requirements of the performance indicators. In addition, by using the idea of adaptive dynamic programming (ADP) at the local level and designing the data set at the collaborative level, the model-free control of the multi-agent system is realized. In the data acquisition phase, intermittent noise detection is used to suppress the system oscillation caused by the introduced external noise. Finally, the effectiveness of the proposed control protocol and algorithm is verified by simulation on the F-16 aircraft model. © 2024 ieee.

关键词： Multi agent systems

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Convolutional Neural Network-based image tamper detection with Error Level Analysis 2

Convolutional Neural Network-based image tamper detection wi...

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2nd ieee international conference on intelligent and Innovative Technologies in computing, Electrical and Electronics, ICIITCEE 2024

作者： Manjunatha, S. Swetha, M.D. Rashmi, S. Subramanian, Ananda Kumar Vinoth Kumar, V. Mallikarjuna Swamy, S. Global Academy of Technology Department of Information Science and Engineering Bengaluru India BNM Institute of Technology Department of Computer Science and Engineering Bengaluru India Bengaluru India Vit - University School of Computer Science Engineering Dept of Computational Intelligence Vellore India Vit - University School of Computer Science Engineering and Information Systems Vellore India Jss Academy of Technical Education Dept of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350306415

Photography is the most important, powerful, and reliable means of expression. Today, digital images not only provide disinformation but also act as agents for secret communication. Users and editing professionals work with digital images for a variety of purposes. Images are often regarded as facts or proof of reality, so they are misleading and fake news or publications of any form that use images manipulated in a highly misleading way. To recognize image tampering needs multiple image data and a model that can handle all the pixels in the image. Furthermore, training the data more efficiently and needed flexibility support everyday use. Models based on Deep learning such as Convolutional Neural Networks with error level analysis (ELA) are the perfect solution. © 2024 ieee.

关键词： Convolutional Neural Network Deep Learning Efficiency Error Level Analysis Image Forgery Detection

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PowerSched - managing power consumption in overprovisioned systems

PowerSched - managing power consumption in overprovisioned s...

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2024 international conference on Cluster computing

作者： Simmendinger, Christian Marquardt, Marcel Maeder, Jan Schneider, Ralf Hewlett Packard Enterprise Ratingen Germany High Performance Comp Ctr Stuttgart Stuttgart Germany

ISBN: (纸本)9798350383461;9798350383454

Over the last decade both capital costs for the acquisition of modern HPC systems as well as costs to power and cool these systems have increased significantly. In addition to direct operational costs, the investment costs for the infrastructure required to provide the necessary energy and cooling capacity have also risen substantially. In order to shift the ratio between infrastructure costs and system costs in favor of the latter, the High Performance computing Center Stuttgart (HLRS) has decided to size the system Hawk based on energy requirements of typical user workloads. To not overload the data centre's infrastructure the power consumption of the Hawk system has to be constrained. The Hawk system hence is operated as an over-provisioned system where nameplate thermal design power exceeds provisioned power. In this work we explain how the dynamic power management solution called PowerSched which is implemented by Hewlett Packard Enterprise (HPE) at HLRS, is able to balance compute jobs with moderate demands for CPU-power against CPU-power hungry jobs and thus keeps the overall power consumption of the system and its components within the required limits. We elaborate how the solution minimizes power consumption while maximizing the overall performance of all applications executed in the system at any given time. We further demonstrate the effectiveness of the chosen approach and the effects of PowerSched on power consumption at system and component level. Finally, the performance impact at application level is compared against executions with static power capping per CPU and against executions with unconstrained power.

关键词： Power Management Overprovisioned systems Energy Efficiency Dynamic Golden Section Search

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