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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5844-5854页

作者： Patel, Manali Jariwala, Krupa Chattopadhyay, Chiranjoy Sardar Vallabhbhai National Institute of Technology Department of Computer Science and Engineering Surat395007 India FLAME University School of Computing and Data Sciences Pune412115 India

An accurate estimation of future stock prices can help investors maximize their profits. The current advancements in the area of artificial intelligence (AI) have proven prevalent in the financial sector. Besides, stock market prediction is difficult owing to the considerable volatility and unpredictability induced by numerous factors. Recent approaches have considered fundamental, technical, or macroeconomic variables to find hidden complex patterns in financial data. At the macro level, there exists a spillover effect between stock pairs that can explain the variance present in the data and boost the prediction performance. To address this interconnectedness defined by intrasector stocks, we propose a hybrid relational approach to predict the future price of stocks in the American, Indian, and Korean economies. We collected market data of large-, mid-, and small-capitalization peer companies in the same industry as the target firm, considering them as relational features. To ensure efficient feature selection, we have utilized a data-driven approach, i.e., random forest feature permutation (RF2P), to remove noise and instability. A hybrid prediction module consisting of temporal convolution and linear model (TCLM) is proposed that considers irregularities and linear trend components of the financial data. We found that RF2P-TCLM gave the superior performance. To demonstrate the real-world applicability of our approach in terms of profitability, we created a trading method based on the predicted results. This technique generates a higher profit than the existing approaches. © 2024 IEEE.

关键词： Profitability

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An Efficient Capsule Based Neural Network for Detection of Grape Plant Leaf Disease and Disease Stages 5

An Efficient Capsule Based Neural Network for Detection of G...

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5th International Conference on Emerging Systems and Intelligent computing, ESIC 2025

作者： Vasudevan, N. Praveenkumar, S. Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems Tamilnadu kattankulathur India

ISBN: (纸本)9798331522100

Plant infections destroy and impair the quality of crops, and the pesticides used to treat them pollute the soil, rendering it unfit for planting. Image processing and deep learning technologies may be used to identify disease spots on grape leaves. The detection's precision and efficiency, on the other hand, remain problems. In this paper, the performance of a few well-known CNN models implemented utilizing transfer learning, such as ResNet18, VGG16, and GoogleNet were compared with capsule network. The local entity characteristics are first found by the Capsule layers. For the purpose of collecting aggregate data like "disease type"and "disease stage,"the capsule layer makes use of geographic information and the frequency of local-level characteristics. Colored pictures of strong and unhealthy leaves were taken from the public dataset and then used to train the models. The proposed work focused on the novelty concept of identifying the stages of diseases. The Capsule based classification technique can give competitive benefits with an effective classification of accuracy and stages of leaf disease related to other models. © 2025 IEEE.

关键词： Agricultural engineering classifications diseases image processing neural networks

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SMART AGRICULTURE: INTEGRATING AIR QUALITY MONITORING WITH DEEP LEARNING FOR PROCESS OPTIMIZATION*

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Scalable computing 2025年第3期26卷 1005-1016页

作者： Shobana, J. Subramanian, A Venkata Balamurugan, P. Perumal, Sivakumar Sankari, V. Eldho, K.J. Nareshkumar, R. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India Department of Computer Science and Engineering GITAM University Bengaluru India Department of Computer Science and Engineering J N N Institute of Enginering India Saveetha university Chennai India Department of Computer Science and Engineering Sasi Institute of Technology and Engineering Andhra Pradesh Tadepalligudem India Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Kariyamanickam Rd Tamil Nadu Samayapuram India PG and Research Department of Computer Science Mary Matha Arts and Science College Kerala Mananthavady India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India

Modernization and intense industrialization have led to a substantial improvement in people’s quality of life. However, the aspiration for achieving an improved quality of life results in environmental contamination. A primary consequence of environmental degradation is air pollution, resulting from rising levels of poisonous chemicals in the atmosphere, which may induce detrimental health conditions in humans. It is harmful to both humans and agriculture. Given that the effects of air pollution on plants may not be readily apparent, it is important to analyse the necessary data and compute the outcomes. Farmers prioritise on pests and plant diseases, frequently neglecting the detrimental impacts of air pollution. Some plant species can withstand high amounts of pollution from suspended particulate matter and accumulated gases, while others are more susceptible to harm. Therefore, plants’ reaction to air pollution is influenced by the kind of harmful compounds, their levels, and the plant’s susceptibility to them. The LSTM +CNN Proposed Ensemble method may be used to analyse the impact of air pollution on agriculture by examining trends in crop production over time and predicting which crop is more resistant based on the pollution data. The initiative created for this aim may assist farmers in determining the most suitable crop to cultivate in their fields to minimize the impact of air pollution on agricultural yield. The findings show deep learning algorithms correctly predict hourly pollutant concentrations such as carbon monoxide, sulphur dioxide, nitrogen dioxide, ground-level ozone, and particulate matter 2.5, along with the hourly Air Quality Index (AQI) for California. A proposed model used test RMSE values as a measure to evaluate prediction performance, achieving the best possible results. © 2025 SCPE.

关键词： Nitrogen oxides

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Expanding the Vocabulary of BERT for Knowledge Base Construction 1

Expanding the Vocabulary of BERT for Knowledge Base Construc...

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1st Workshop on Knowledge Base Construction from Pre-Trained Language Models and the 2nd Challenge on Language Models for Knowledge Base Construction, KBC-LM + LM-KBC 2023

作者： Yang, Dong Wang, Xu Celebi, Remzi Institute of Data Science Department of Advanced Computing Sciences Maastricht University Netherlands

Knowledge base construction entails acquiring structured information to create a knowledge base of factual and relational data, facilitating question answering, information retrieval, and semantic understanding. The challenge called"Knowledge Base Construction from Pretrained Language Models" at International Semantic Web Conference 2023 defines tasks focused on constructing knowledge base using language model. Our focus was on Track 1 of the challenge, where the parameters are constrained to a maximum of 1 billion, and the inclusion of entity descriptions within the prompt is prohibited. Although the masked language model offers sufficient flexibility to extend its vocabulary, it is not inherently designed for multi-token prediction. To address this, we present Vocabulary Expandable BERT for knowledge base construction, which expand the language model’s vocabulary while preserving semantic embeddings for newly added words. We adopt task-specific re-pre-training on masked language model to further enhance the language model. Through experimentation, the results show the effectiveness of our approaches. Our framework achieves F1 score of 0.323 on the hidden test set and 0.362 on the validation set, both data set is provided by the challenge. Notably, our framework adopts a lightweight language model (BERT-base, 0.13 billion parameters) and surpasses the model using prompts directly on large language model (Chatgpt-3, 175 billion parameters). Besides, Token-Recode achieves comparable performances as Re-pretrain. This research advances language understanding models by enabling the direct embedding of multi-token entities, signifying a substantial step forward in link prediction task in knowledge graph and metadata completion in data management. 1 © 2023 CEUR-WS. All rights reserved.

关键词： Information management

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Feature Selection in Learning to Rank for Listwise Approach using Multiobjective Evolutionary Algorithm

Feature Selection in Learning to Rank for Listwise Approach ...

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2024 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Amala, K.J. Rajeswari, D. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543617

Information retrieval systems commonly depend on ranking models to prioritize search results, which requires efficient feature selection to improve accuracy. Feature selection improves the model's query relevance ranking by selecting the most relevant features. This unique method avoids diluting the ranking model with unnecessary variables, resulting in more accurate ranks. The proposed approach consists of three separate stages: multi-objective instance selection, feature selection, and ensemble method. By utilizing a listwise methodology, the algorithm directly enhances the optimization of ranking criteria resulting in enhanced performance. The effectiveness of FS-listwise was evaluated using standardized datasets. After conducting experiments, results shown that the proposed method have peak performance than MOFSRank and classical ranking algorithms, such as ListNet and LambdaMART, in terms of Normalized Discounted Cumulative Gain and consistently selecting a minimal yet highly effective feature set. © 2024 IEEE.

关键词： Contrastive Learning

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Exact Multi-objective Path Finding with Negative Weights 34

Exact Multi-objective Path Finding with Negative Weights

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34th International Conference on Automated Planning and Scheduling, ICAPS 2024

作者： Ahmadi, Saman Sturtevant, Nathan R. Harabor, Daniel Jalili, Mahdi School of Engineering RMIT University Australia Department of Computing Science University of Alberta Canada Department of Data Science and AI Monash University Australia

ISBN: (纸本)9781577358893

The point-to-point Multi-objective Shortest Path (MOSP) problem is a classic yet challenging task that involves finding all Pareto-optimal paths between two points in a graph with multiple edge costs. Recent studies have shown that employing A* search can lead to state-of-the-art performance in solving MOSP instances with non-negative costs. This paper proposes a novel A*-based multi-objective search framework that not only handles graphs with negative costs and even negative cycles but also incorporates multiple speed-up techniques to enhance the efficiency of exhaustive search with A*. Through extensive experiments, our algorithm demonstrates remarkable success in solving difficult MOSP instances, outperforming leading solutions by several factors. Copyright © 2024, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Pareto principle

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An Efficient Technique for Feature Selection in Learning to Rank Using NSGA-II

An Efficient Technique for Feature Selection in Learning to ...

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2024 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543617

Feature Selection (FS) is essential for optimizing Learning to Rank (LTR) models by determining the vital choice of attributes from a complex dataset. This study examines feature selection strategies within the framework of learning to rank, specifically emphasizing a multi-objective evolutionary algorithm utilizing the listwise approach. Conventional ranking algorithms frequently experience issues with superfluous or unnecessary features, undermining their efficacy. The suggested method employs a combination of instance selection, evolutionary optimization, and ensemble techniques to choose feature subsets. The methodology seeks to reduce the feature count while enhancing ranking precision, assessed via the Normalized Discounted Cumulative Gain (NDCG) metric. Experiments were performed using LETOR 4.0 dataset (MQ2007-list and MQ2008-list), evaluating proposed method against established listwise ranking algorithms like ListMLE, ListNet, RankCosine and p-ListMLE. The findings indicate that, although there are performance discrepancies relative to current algorithms, the suggested method attains substantial feature reduction up to 60 percent while maintaining competitive ranking accuracy. © 2024 IEEE.

关键词： Optimization algorithms

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SACNN-IDS: A self-attention convolutional neural network for intrusion detection in industrial internet of things

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1398-1411页

作者： Mimonah Al Qathrady Safi Ullah Mohammed S.Alshehri Jawad Ahmad Sultan Almakdi Samar M.Alqhtani Muazzam A.Khan Baraq Ghaleb Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia Department of Computer Science Quaid-i-Azam UniversityIslamabadPakistan Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia School of Computing Engineering and the Built EnvironmentEdinburgh Napier UniversityEdinburghUK ICESCO Chair Big Data Analytics and Edge Computing Quaid-i-Azam UniversityIslamabadPakistan

Industrial Internet of Things(IIoT)is a pervasive network of interlinked smart devices that provide a variety of intelligent computing services in industrial *** IIoT nodes operate confidential data(such as medical,transportation,military,etc.)which are reachable targets for hostile intruders due to their openness and varied *** Detection Systems(IDS)based on Machine Learning(ML)and Deep Learning(DL)techniques have got significant ***,existing ML and DL-based IDS still face a number of obstacles that must be *** instance,the existing DL approaches necessitate a substantial quantity of data for effective performance,which is not feasible to run on low-power and low-memory *** and fewer data potentially lead to low performance on existing *** paper proposes a self-attention convolutional neural network(SACNN)architecture for the detection of malicious activity in IIoT networks and an appropriate feature extraction method to extract the most significant *** proposed architecture has a self-attention layer to calculate the input attention and convolutional neural network(CNN)layers to process the assigned attention features for *** performance evaluation of the proposed SACNN architecture has been done with the Edge-IIoTset and X-IIoTID *** datasets encompassed the behaviours of contemporary IIoT communication protocols,the operations of state-of-the-art devices,various attack types,and diverse attack scenarios.

关键词： convolutional neural network deep learning industrial internet of things intrusion detection self-attention

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You Are the First Line of Defense: Integrating Humans into Information Security Frameworks

You Are the First Line of Defense: Integrating Humans into I...

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2024 International Conference on Computer and Applications, ICCA 2024

作者： Al-Mohammed, Bashayer Abdulla Baydoun, Ali Oryx Universal College in Partnership with Liverpool John Moores University Department of Computing and Data Science Doha Qatar

ISBN: (纸本)9798350367560

This research aimed to understand the current level of security training and cultural awareness among employees across different organizations. The research draws on data collected through a comprehensive questionnaire between employees, providing insights into the relationship between demographic factors and information security awareness. The findings highlight the need for tailored training programs incorporating cultural nuances to mitigate risks effectively. This research contributes to developing adaptable and relevant information frameworks, emphasising users' critical role as the first line of defence against cyber threats. The findings of this research will benefit various stakeholders, such as Governments, semi-governments, and private and non-government organizations. © 2024 IEEE.

关键词： Awareness Culture Cyber Threats Cybersecurity Framework Human Element Information Security Training

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Resilience of Aviation Cyber-Physical Systems to Malicious Attacks

Resilience of Aviation Cyber-Physical Systems to Malicious A...

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2024 International Conference on Computer and Applications, ICCA 2024

作者： Abozaid, Fatemah Baydoun, Ali Oryx Universal College in Partnership with Liverpool John Moores University Department of Computing and Data Science Doha Qatar

ISBN: (纸本)9798350367560

The inter-connectivity of critical aviation operations are the prime targets for Advance Persistent Threats (APTs) and others malicious actors due to increasingly relying on the Cyber-Physical Systems (CPS). This paper examines the current cyber resilience of aviation CPS to such cyber threats, emerging trends, and proposing new and effective resilience strategies. Using a multi-pronged approach, data were collected through survey from the aviation professionals in the state of Qatar. Quantitative analysis was conducted on survey data to assess trends in threat perception and resilience measures, while qualitative analysis of interviews provided in-depth insights into current vulnerabilities and effective mitigation strategies show that outdated software, insufficient access controls, and complex supply chains increase the risk to advanced persistent threats (APTs) and other sophisticated attacks. Our findings can assist the industry leaders, policymakers, and cybersecurity professionals to get valuable insights to safeguard the aviation ecosystem against the growing cyber threats. © 2024 IEEE.

关键词： Cyber attacks

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