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Design and Development of Artificial Intelligence Framework to Forecast the Security Index Direction and Value in Fusion with Sentiment Analysis of Financial News

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SN Computer science 2024年第6期5卷 787页

作者： Singh, Harmanjeet Malhotra, Manisha Singh, Supreet Sharma, Preeti Prabha, Chander University School of Computing Rayat Bahra University University Institute of Computing Chandigarh University School of Computer Science UPES Chitkara University Institute of Engineering and Technology Chitkara University

The domain of stock price prediction is extensively researched owing to its complex data structure and numerous influential factors. In the current epoch, many modern financial applications demonstrate non-linear and uncertain characteristics that exhibit temporal variability. As a result, there has been a notable rise in the need for resolutions to highly non-linear and time-variant issues. External factors, including public sentiment and political events, can influence stock market trends. The primary aim of this research is to propose a novel framework for predicting the blue-chip stock price in the future by combining historical stock data corresponding to the financial news published regarding stock in the newspaper. The initial step involves integrating sentiment and situational features into a machine-learning model. This integration aims to evaluate public sentiments’ influence on algorithms’ predictive precision thirty days ahead. Furthermore, regression models are utilized to analyze the inter-dependencies that exist among companies. To facilitate an experimental analysis, the researchers acquired historical data from the stock market through reputable sources such as the National Stock Exchange-India and collected news data about stock from the financial newspaper “EconomicTimes”. A series of machine learning models were tested to facilitate the classification of news sentiments of the stocks, followed by the fusion of resultant polarities with the stock’s historical data on the standard column “DATE” and then tested series of deep learning models to observe the impact of news on stock price and by taking reference of this, the authors successfully predicted the stock price on a random day. The findings unequivocally indicate that the proposed research design produces better results than other state-of-the-art models. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Comparative analysis Economics Sentiment analysis Stock news Stock price forecast

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Fall Detection using LiDAR Sensor with PointNet++ 4

Fall Detection using LiDAR Sensor with PointNet++

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

作者： Ayyagari, Anish Sai Ahmed, Syed Ashfaq Venkatakrishnan, Shri Thangavel, Senthil Kumar Jeyakumar, G. Nalluri, Madhusudana Rao Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9798331523923

In today's world, fall incidents pose a significant risk to the elderly, often resulting in severe consequences. Falls can cause damage to the body, especially in elders, where it can be *** is vital to detect falls in order to provide assistance. The simple solution is to install cameras in required areas, which would constantly monitor the area for any falls occurring, this poses a privacy issue in high-risk regions such as bathrooms and toilets. Keeping this in mind, this paper proposes the use of LiDAR sensors that do not cause any invasion of privacy for fall detection. The paper further uses the Pointnet++ to detect fall from the LiDAR scans. The model achieves an accuracy of 85.1 % and precision of 84.8 % on a 70-30 split of the dataset and an accuracy of 86.3 % and precision of 86.2 % on an 80-20 split of the dataset. © 2025 IEEE.

关键词： Fall detection

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Securing Wireless Communication: A Comprehensive Arduino-based Approach 3

Securing Wireless Communication: A Comprehensive Arduino-bas...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Navaneeth, M. Vamsi Krishna, K. Belwal, Meena Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

This research aims at developing Arduino solutions to improve the security of wireless communication systems for this work and to present a broad intervention plan for achieving this aim. Incorporating the features of Arduino microcontrollers the system has the potential of mitigating some of the weak wireless communications paradigms. The methodology consists in applying the use of such measures as the encryption algorithms and authentication and the use of secure data transmission protocols. The approach, proposed, not only enhances the security of wireless communication but also is cost effective and easy to install. In this respect, it is hoped that the outcomes of the project will offer effective and efficient solutions for implementing quality security elements for wireless transmission with practical and experimental *** purpose of this work is to generate and transmit the text message through a wireless communication system in a secured way. This facility prevails in the mobile network for which the user pays for it, whereas the system designed here can be used as personal communication system and information can be transmitted at free of *** result of the work is expected to be a secure information transmission with wireless technology implemented and encrypt the data that need to be transmitted without letting interception of a third party and reach the destination, convert and decrypt the message into readable *** work more particularly targets at reducing some of the risks associated with present wireless communication systems through use of effective encryption and authentication. The results are expected to include a major decrease in the interception risks, as well as the effective and safe text message transmission with real-world evidence as proof. © 2025 IEEE.

关键词： Microcontrollers

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GAVx at SemEval-2024 Task 10: Emotion Flip Reasoning via Stacked Instruction Finetuning of LLMs 18

GAVx at SemEval-2024 Task 10: Emotion Flip Reasoning via Sta...

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18th International Workshop on Semantic Evaluation, SemEval 2024, co-located with the 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics, NAACL 2024

作者： Nguyen, Vy Zhang, Xiuzhen School of Science Engineering & Technology RMIT University Australia School of Computing Technologies RMIT University Australia

ISBN: (纸本)9798891761070

The Emotion Flip Reasoning task at SemEval 2024 aims at identifying the utterance(s) that trigger a speaker to shift from an emotion to another in a multi-party conversation. The spontaneous, informal, and occasionally multilingual dynamics of conversations make the task challenging. In this paper, we propose a supervised stacked instruction-based framework to finetune large language models to tackle this task. Utilising the annotated datasets provided, we curate multiple instruction sets involving chain-of-thoughts, feedback, and self-evaluation instructions, for a multi-step finetuning pipeline. We utilise the self-consistency inference strategy to enhance prediction consistency. Experimental results reveal commendable performance, achieving mean F1 scores of 0.77 and 0.76 for triggers in the Hindi-English and English-only tracks respectively. This led to us earning the second highest ranking in both tracks. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Survey and Tutorial on Hybrid Human-Artificial Intelligence

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Tsinghua science and Technology 2023年第3期28卷 486-499页

作者： Feifei Shi Fang Zhou Hong Liu Liming Chen Huansheng Ning School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China School of Computer Science and Software Engineering East China Normal UniversityShanghai 200062China Faculty of Computing Ulster UniversityNewtownabbey BT370QBUK

The growing computing power,easy acquisition of large-scale data,and constantly improved algorithms have led to a new wave of artificial intelligence(AI)applications,which change the ways we live,manufacture,and do *** with this development,a rising concern is the relationship between AI and human intelligence,namely,whether AI systems may one day overtake,manipulate,or replace *** this paper,we introduce a novel concept named hybrid human-artificial intelligence(H-AI),which fuses human abilities and AI capabilities into a unified *** presents a challenging yet promising research direction that prompts secure and trusted AI innovations while keeping humans in the loop for effective *** scientifically define the concept of H-AI and propose an evolution road map for the development of AI toward *** then examine the key underpinning techniques of H-AI,such as user profile modeling,cognitive computing,and human-in-the-loop machine ***,we discuss H-AI’s potential applications in the area of smart homes,intelligent medicine,smart transportation,and smart ***,we conduct a critical analysis of current challenges and open gaps in H-AI,upon which we elaborate on future research issues and directions.

关键词： hybrid human-artificial intelligence(H-AI) Internet of Things(IoT) artificial intelligence(AI)

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Predicting and Evaluating Anomaly Detection and Traffic Analysis on Software Defined Networks Using a Hybrid Machine Learning Approach 5th

Predicting and Evaluating Anomaly Detection and Traffic Anal...

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5th International Conference on computing and Network Communications, CoCoNet 2023

作者： Darshan, S. Radhika, N. Radhika, G. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9789819745395

In the rapidly evolving landscape of Software Defined Networks (SDNs), network security is of paramount importance. This research paper presents a comprehensive study on anomaly detection and traffic analysis in SDNs using machine learning algorithms. The study addresses a critical research gap by proposing an effective methodology for detecting network anomalies and analyzing traffic patterns. Leveraging a carefully selected dataset, we evaluate the performance of Logistic Regression, Decision Tree, Random Forest, Neural Networks and Hybrid Models. The results demonstrate the superior performance of the hybrid model built using Decision Tree and Neural Networks and the competitive accuracy of Neural Networks in SDN anomaly detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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IoT Environmental Monitoring with Anomaly Detection 3

IoT Environmental Monitoring with Anomaly Detection

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Abhiram, D. Dhanush Reddy, K. Koushik Reddy, P. Rajagopal, Shinu Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bangalore India

ISBN: (纸本)9798331527549

This project proposes an IoT-based environmental evaluation system taking into consideration its surroundings through a microcontroller ESP8266 and sensor DHT11. The system reads the current temperature and humidity in addition to providing alerts for changes as well as threshold and sensor integrity issues. It contains context awareness and adaptive monitoring. It connects to the Arduino IoT Cloud, which allows users to monitor data in real-time, or receive notifications, or set up thresholds right from the mobile or web application. The features such as real-time monitoring, real-time anomaly detection, and remote access make this project practical as the key application for smart homes, farming and industries applications. © 2025 IEEE.

关键词： Microcontrollers

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Federated Learning based Gender Classification in Heterogeneous and Distributed Data having Concept Drift 4

Federated Learning based Gender Classification in Heterogene...

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4th International Conference on Evolutionary computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Sharma, Vishwash Reddy Challa, Venkatahemant Kumar Pranavi, Pasupuleti Singh, Rimjhim Padam Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

Federated Learning is gaining significant traction in recent times, due to its ability to protect privacy while giving comparable results across various use cases. However, it often falls short in delivering good results in scenarios where the client devices at different places store huge and heterogeneous data. The studies explore the effectiveness of federated learning, when utilized for model training on heterogeneous data having concept drift nature and being placed on different client devices. The paper proposes a novel combination of EfficientNet-B0 model fine-tuned with adaptive and weighted normalization layers in a federated learning setup for gender classification in heterogeneous Fairface dataset having human images from different races. Several other Convolutional Neural Network (CNN) models namely, ResNet50, DenseNet121, GoogLeNet, AlexNet etc. have also been trained in different traditional and federated setups. Experiments revealed that EfficientNet-B0 outperformed the other models trained in all the scenarios with an outperforming accuracy of 88.18% in the traditional federated setup and 90.56% when fine-tuned using normalization methods and evaluated in a federated setup with four client devices. These findings highlight the potential of normalization techniques to improve the effectiveness of federated learning, especially when dealing with concept drift data. © 2025 The Authors. Published by Elsevier B.V.

关键词： Federated learning

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Leveraging Sequential Deep Learning Models for Robust Compiler Bug Detection in Software Logs 3

Leveraging Sequential Deep Learning Models for Robust Compil...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Mishra, Sahil Tiwari, Utkarsh Kavitha, C.R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Compiler bugs critically impact the correctness of software applications, making their detection and resolution vital for improving software quality. This paper proposes a novel approach leveraging sequential deep learning models, including Long short-term memory, Gated recurrent unit, Recurrent neural network, and a stacked ensemble model, to enhance compiler bug identification. By combining the strengths of these architectures, the proposed method improves predictive accuracy and generalization. To ensure model interpretability, we employ LIME (Local Interpretable Model-agnostic Explanations) to identify the features influencing bug detection decisions. Experimental results demonstrate that the stacked ensemble outperforms individual models in terms of precision, recall, F1-score, and ROC-AUC. This work advances the state of compiler log analysis and contributes significantly to software quality assurance by integrating robust deep learning techniques with explainable AI. © 2025 IEEE.

关键词： Application programs

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Adaptive Simplified Chicken Swarm Optimization Based on Inverted S-Shaped Inertia Weight

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Chinese Journal of Electronics 2022年第2期31卷 367-386页

作者： GU Yanchun LU Haiyan XIANG Lei SHEN Wanqiang School of Science Jiangnan University Wuxi Engineering Technology Research Center for Biological Computing Jiangnan University

Considering the issues of premature convergence and low solution accuracy in solving high-dimensional problems with the basic chicken swarm optimization algorithm, an adaptive simplified chicken swarm optimization algorithm based on inverted S-shaped inertia weight(ASCSO-S) is proposed. Firstly, a simplified chicken swarm optimization algorithm is presented by removing all the chicks from the chicken swarm. Secondly, an inverted S-shaped inertia weight is designed and introduced into the updating process of the roosters and hens to dynamically adjust their moving step size and thus to improve the convergence speed and solution accuracy of the algorithm. Thirdly, in order to enhance the exploration ability of the algorithm, an adaptive updating strategy is added to the updating process of the *** experiments on 21 classical test functions show that ASCSO-S is superior to the other comparison algorithms in terms of convergence speed, solution accuracy, and solution stability. In addition, ASCSO-S is applied to the parameter estimation of Richards model, and the test results indicate that ASCSO-S has the best fitting results compared with other three algorithms.

关键词： particle swarm optimisation solution stability adaptive simplified chicken swarm optimization algorithm parameter estimation Richards model solution accuracy swarm intelligence inverted S-shaped inertia weight convergence speed adaptive updating strategy convergence updating process ASCSO-S

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