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

作者： Rajalakshmi, N.R. Sharmila, L. Prabu, S. Veeramakali, T. Arulkumaran, G. Chourasia, Pranshu Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India School of Computing Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Chennai India School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India School of Computing and Information Technology Reva University Karnataka Bangalore560064 India

ISBN: (纸本)9798331523923

The HVAC system, energy storage building, distributed power supply, and other equipment are integrated into the scheduling algorithm, which is aimed at reducing household electricity consumption. It is also assumed that users can provide energy to the grid according to their own conditions. Taking electricity cost and comfort level as optimization targets, a home energy optimization control model for the coordinated management of hybrid energy sources is built. A smart scheduling mechanism based on the improved adaptive particle swarm optimization approach is proposed in order to derive the best time intervals for electric appliances, necessary power for the control of the room temperature for every time frame, and power for charging and discharging of the storage battery at various moments. Simulation results show that through the incorporation of distributed photovoltaic power generation, backup storage by battery, and home energy optimization control, the system efficiently balances between user comfort and electricity consumption. This offers great technical support to the development of home energy management systems. By using time-of-use electricity price for energy acquisition and supply, the optimization control goal is minimizing both power use and cost as well as preserving comfort levels. The hybrid energy management's proposed home energy optimization control model uses an adaptive particle swarm optimization algorithm to find the optimal operation schedules of the electrical appliances, the required power for temperature control in a room, and the charge/discharge power level of the storage battery at each time interval. As per the optimization principle, the proposed dynamic programming algorithm converts the multi-stage problem into a sequence of single-stage problems and solves them separately. This method successfully resolves intricate problems that cannot be addressed through greedy algorithms or divide-and-conquer. In this research, management ac

关键词： Decision making

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CareerBoost: A Hybrid RAG-NLP Job Recommendation Framework 8

CareerBoost: A Hybrid RAG-NLP Job Recommendation Framework

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Deshmukh, Sanskruti Bajaj, Aaryaman Vishwakarma Institute of Technology Department of Artificial Intelligence and Data Science Pune India Srm Institute of Science and Technology Department of Computing Technologies Chennai Kattankulathur India

ISBN: (纸本)9798350376425

The proposed system presents a novel approach to job recommendation and resume enhancement, leveraging advanced Natural Language Processing (NLP) techniques and Retrieval-Augmented Generation (RAG). We introduce a comprehensive system that combines semantic search, FAISS-based similarity matching, and a fine-tuned GPT-2 model to provide personalized job recommendations and actionable resume improvement suggestions. The evaluation, conducted through offline metrics and online A/B testing with 5,000 users, demonstrates significant improvements over traditional keyword-matching approaches. The RAG-based system achieved a 25.8% increase in recommendation relevance (Precision@10), a 45.7% increase in user engagement, and a 41.2% increase in job application rates. Furthermore, users who applied the system's resume enhancement suggestions saw a 9.0% improvement in subsequent job matches. These results highlight the potential of AI-driven systems to revolutionize online recruitment and career development, while also emphasizing the need for ongoing consideration of ethical implications and fairness in AI-assisted hiring processes. © 2024 IEEE.

关键词： Semantics

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Ship AIS Trajectory Outlier Identification Model under High-speed Sampling Conditions 4

Ship AIS Trajectory Outlier Identification Model under High-...

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4th International Symposium on Computer Technology and Information science, ISCTIS 2024

作者： Wang, Han Chen, Qilong Jimei University College of Science Xiamen China Jimei University Digital Fujian Big Data Modeling and Intelligent Computing Institute College of Science Xiamen China

ISBN: (纸本)9798350354560

In order to efficiently identify the outliers of ship AIS trajectories under high-speed sampling conditions, this paper proposes an outlier identification model based on ship position distance distribution. Taking the ship AIS track data of Yangshan port as an example, the kernel density is used to estimate the daily ship position distance distribution, and then the threshold is determined and the outliers are screened out. In this paper, it is proved that the ship position distance distributions on two adjacent days are approximate, so the threshold value of yesterday can be substituted into today to identify today's outliers effectively. The experimental results show that in the application environments of Yangshan Port waters where the average rate of ship AIS track data writing into the database is 43 items/sec and the coastal waters of East China Sea where the average rate of ship AIS track data writing into the database is 287 items/sec, the model is able to efficiently identify the outliers of the ship AIS trajectories. © 2024 IEEE.

关键词： Trajectories

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Granular Sequential Three-Way Decision for Specific Decision Classes

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IEEE Transactions on Fuzzy Systems 2025年第5期33卷 1650-1663页

作者： Cheng, Yunlong Yang, Xiuhua Zhang, Qinghua Shao, Yabin Wang, Guoyin Chongqing University of Posts and Telecommunications School of Science Chongqing400065 China Chongqing University of Posts and Telecommunications Chongqing Key Laboratory of Tourism Multisource Data Perception and Decision Ministry of Culture and Tourism Key Laboratory of Big Data Intelligence Computing Chongqing Key Laboratory of Computational Intelligence Chongqing400065 China

Sequential three-way decision (S3WD) is an efficient granular computing paradigm for dealing with uncertain problems. However, it is primarily oriented to all decision classes, which contradicts the fact that decisions are typically for the specific decision classes. Meanwhile, most S3WD models hide the topological structure of the granules, leading to difficulties in semantic interpretation. To address the issues, integrating model construction, attribute reduction and knowledge extraction, a general framework of granular sequential three-way decision (GrS3WD) for the specific decision classes is proposed to improve semantic interpretation and computational efficiency. First, a two-stage trisecting strategy and a GrS3WD model are proposed to integrate model construction with attribute reduction. Its main advantage is that it retains the topological structure of granules, which not only enhances semantic interpretation but also avoids unnecessary double counting. Second, three acceleration strategies and a novel granular sequential three-way reduction (GrS3WR) algorithm are proposed to fast obtain a classification-based reduct or a class-specific reduct. Finally, the decision rules with multi-granularity can be directly extracted from the concept tree generated by GrS3WR. Experimental results demonstrate that a class-specific reduct usually has fewer attributes and better classification performance than a classification-based reduct. Moreover, GrS3WR can significantly improve the computational efficiency of attribute reduction. © 1993-2012 IEEE.

关键词： Granular computing

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SM2PNet: A Deep Learning Approach Towards Indian Stock Market Movement Prediction 8

SM2PNet: A Deep Learning Approach Towards Indian Stock Marke...

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

作者： Patel, Manali Jariwala, Krupa Chattopadhyay, Chiranjoy SVNIT Department of CSE Surat India FLAME University School of Computing and Data Science Pune India

ISBN: (纸本)9798350334012

The objective of this study is to forecast the future price movements of equities listed on the National Stock Exchange (NSE) of India. To anticipate the future price movement, all prior statistical and machine learning techniques have focused on identifying latent temporal dependencies. In actuality, stock prices reflect all available information and are influenced by several external variables, making forecasting difficult. One such major element is the interdependence that exists between stock pairings from the same industry. By incorporating this relational dependence combined with the temporal dependency, we may attain improved prediction performance. In order to demonstrate this, we have created a model, SM2PNet (Stock Market Movement Prediction Network), which takes into consideration the characteristics of highly correlated firms in order to examine their impact on the stock price of the selected target company. We've analysed predefined sector-industry linkages in order to identify the most relevant peer firms. Sequential model long short-term memory (LSTM) is used to represent temporal dependence. The investigation confirms that SM2PNet beats all other available deep learning methods. © 2023 IEEE.

关键词： Financial markets

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Secure Resource Allocation via Constrained Deep Reinforcement Learning 6th

Secure Resource Allocation via Constrained Deep Reinforceme...

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6th International Conference on Machine Learning for Cyber Security, ML4CS 2024

作者： Sun, Jianfei Gao, Qiang Wu, Cong Li, Yuxian Wang, Jiacheng Niyato, Dusit School of Computing and Information Systems Singapore Management University Singapore188065 Singapore School of Computing and Artificial Intelligence Southwestern University of Finance and Economics Chengdu611130 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

ISBN: (纸本)9789819645657

The proliferation of Internet of Things (IoT) devices and the advent of 6G technologies have introduced computationally intensive tasks that often surpass the processing capabilities of user devices. Efficient and secure resource allocation in serverless multi-cloud edge computing environments is essential for supporting these demands and advancing distributed computing. However, existing solutions frequently struggle with the complexity of multi-cloud infrastructures, robust security integration, and effective application of traditional deep reinforcement learning (DRL) techniques under system constraints. To address these challenges, we present SARMTO, a novel framework that integrates an action-constrained DRL model. SARMTO dynamically balances resource allocation, task offloading, security, and performance by utilizing a Markov decision process formulation, an adaptive security mechanism, and sophisticated optimization techniques. Extensive simulations across varying scenarios-including different task loads, data sizes, and MEC capacities-show that SARMTO consistently outperforms five baseline approaches, achieving up to a 40% reduction in system costs and a 41.5% improvement in energy efficiency over state-of-the-art methods. These enhancements highlight SARMTO ’s potential to revolutionize resource management in intricate distributed computing environments, opening the door to more efficient and secure IoT and edge computing applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep reinforcement learning

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Calligraphy Alphabet Perception Using Artificial Intelligence 9

Calligraphy Alphabet Perception Using Artificial Intelligenc...

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9th International Conference on science, Technology, Engineering and Mathematics, ICONSTEM 2024

作者： Venkatesh, S. Jeevitha, D. Gnanaselvi, J. Anitha Srm Institute of Science And Technology Department of Data Science And Business Systems School of Computing Chennai Kattankulathur India Jeppiaar Engineering College Department of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350365092

The human brain has a simple time analyzing and processing images. The brain is able to rapidly deconstruct and distinguish an image's various components when the eye perceives it. With the Convolutional Neural Network (CNN) as its foundation, this research suggests deep learning conceptual models. When the algorithms are compared, it becomes clear that CNN-based classification of handwritten alphabets performs better than other algorithms in terms of accuracy. The Manual Net, Alex Net, and LeNet Architectures are among the CNN algorithms employed in this research. The convulational layer, max pooling, flattening, feature assortment, rectifier lined unit, and completely linked softmaxx layers are each components of the aforementioned designs. The proposed network is tested using an image dataset comprising 530 training photos and 2756 testing images. The top precision and cost-efficient model will be used in the Django context to build a handler line for supplying the appeal to be recognized and obtaining the productivity outcome of recognized appeal. © 2024 IEEE.

关键词： Convolutional neural networks

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Advancing Indian Stock Market Prediction with TRNet: A Graph Neural Network Model

Advancing Indian Stock Market Prediction with TRNet: A Graph...

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2023 IEEE International Conference on Modelling, Simulation and Intelligent computing, MoSICom 2023

作者： Patel, Manali Jariwala, Krupa Chattopadhyay, Chiranjoy SVNIT Department of CSE Surat India School of Computing and Data Science FLAME University Pune India

ISBN: (纸本)9798350393415

Indian stock market is unique with high volatility and complexity, so special approaches are needed for its computational understanding. This study proposes Temporal Relational Network (TRNet), a new Graph Neural Network model designed for Indian stock market prediction. TRNet considers pre-defined industry-sector links and time to predict future stock prices in NIFTY-50 index, a significant measure for Indian stock market. Using a financial knowledge graph and Graph Convolution Network, TRNet effectively captures complexities and relationships in non-Euclidean financial data. TRNet outperforms existing models in different timeframes, showing its importance for Indian stock market. We also explore how connections between stocks affect predictions and provided an explainable framework to understand the working mechanisms of the proposed approach. Combining finance and intelligent computing, TRNet paves the way for possibilities in the areas of computational finance using deep learning. © 2023 IEEE.

关键词： Forecasting

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YOLO-NAS-powered Forest Monitoring System: ANew Frontier in Forest Management 7

YOLO-NAS-powered Forest Monitoring System: ANew Frontier in ...

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7th International Conference on Image Information Processing, ICIIP 2023

作者： Aman Ibrahim Saini, Jyoti Chandigarh University Center of Excellence-Data Science And Cognitive Computing Department of Cse Punjab Mohali India

ISBN: (纸本)9798350371406

Forests and natural preserves are vital biological riches, yet unauthorized invasions pose a serious threat to them. This study describes a revolutionary forest monitoring system that uses the cutting-edge and incredibly effective YOLO-NAS (You Only Look Once Neural Architecture Search) object recognition technology. In comparison to existing models, YOLO-NAS automates model design and offers quicker and more accurate results. The system can effectively identify and track unauthorized people, poachers, illicit loggers in real-time by deploying cellular cameras outfitted with YOLO-NAS. The suggested method has the power to transform forest management and advance conservation efforts. © 2023 IEEE.

关键词： Object detection

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Development of an Enhanced Quality Score Calculation Method for Accurate Assessment of Hotel Quality 5

Development of an Enhanced Quality Score Calculation Method ...

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5th International Conference on Advancements in computing, ICAC 2023

作者： Athurugiriya, A. A. A. Gaweshini Sumathipala, Prasanna Hemachandra, K.M. Tharindu Anupa Sri Lanka Institute of Information Technology Faculty of Computing Department of Data Science Malabe Sri Lanka

ISBN: (纸本)9798350358131

In this research paper, an enhanced hotel quality scoring method (EHQSM), is presented as a revolutionary way to analyze hotel quality. The EHQSM combines sentiment analysis, HOLSERV Plus dimensions classification, and amenity scores to deliver a more thorough review. The study generates an overall sentiment score by doing sentiment analysis on reviews categorized into HOLSERV Plus dimensions then thoughtfully averaging it using dimensionspecific weights collected from a survey. The availability of necessary amenities is considered when calculating the amenity score. The novel feature of this approach is how the amenity score, and sentiment score are carefully combined and efficiently weighted using values from the survey. The result is an improved quality score that accurately and thoroughly describes the quality of hotel services. With the goal of facilitating informed decisions for both hotel owners and attentive tourists, this technique establishes an updated standard for the evaluation of hospitality quality. © 2023 IEEE.

关键词： Sentiment analysis

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