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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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Privacy Protection for Blockchain-Based Healthcare IoT Systems: A Survey

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IEEE/CAA Journal of Automatica Sinica 2024年第8期11卷 1757-1776页

作者： Minfeng Qi Ziyuan Wang Qing-Long Han Jun Zhang Shiping Chen Yang Xiang School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyHawthorn3122VictoriaAustralia Faculty of Data Science City University of MacaoMacaoChina CSIRO Data61 SydneyNew South WalesAustralia

To enable precision medicine and remote patient monitoring,internet of healthcare things(IoHT)has gained significant interest as a promising *** the widespread use of IoHT,nonetheless,privacy infringements such as IoHT data leakage have raised serious public *** the other side,blockchain and distributed ledger technologies have demonstrated great potential for enhancing trustworthiness and privacy protection for IoHT *** this survey,a holistic review of existing blockchain-based IoHT systems is conducted to indicate the feasibility of combining blockchain and IoHT in privacy *** addition,various types of privacy challenges in IoHT are identified by examining general data protection regulation(GDPR).More importantly,an associated study of cutting-edge privacy-preserving techniques for the identified IoHT privacy challenges is ***,several challenges in four promising research areas for blockchain-based IoHT systems are pointed out,with the intent of motivating researchers working in these fields to develop possible solutions.

关键词： Blockchain internet of healthcare things(IoHT) privacy-preserving techniques(PPTs).

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Stylometry-driven framework for Urdu intrinsic plagiarism detection: a comprehensive analysis using machine learning, deep learning, and large language models

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Neural computing and Applications 2025年第9期37卷 6479-6513页

作者： Manzoor, Muhammad Faraz Farooq, Muhammad Shoaib Abid, Adnan Department of Computer Science University of Management and Technology Lahore Pakistan Department of Data Science Faculty of Computing and Information Technology University of the Punjab Lahore Pakistan

Detecting plagiarism in documents is a well-established task in natural language processing (NLP). Broadly, plagiarism detection is categorized into two types (1) intrinsic: to check the whole document or all the passages have been written by a single author;(2) extrinsic: where a suspicious document is compared with a given set of source documents to figure out sentences or phrases which appear in both documents. In the pursuit of advancing intrinsic plagiarism detection, this study addresses the critical challenge of intrinsic plagiarism detection in Urdu texts, a language with limited resources for comprehensive language models. Acknowledging the absence of sophisticated large language models (LLMs) tailored for Urdu language, this study explores the application of various machine learning, deep learning, and language models in a novel framework. A set of 43 stylometry features at six granularity levels was meticulously curated, capturing linguistic patterns indicative of plagiarism. The selected models include traditional machine learning approaches such as logistic regression, decision trees, SVM, KNN, Naive Bayes, gradient boosting and voting classifier, deep learning approaches: GRU, BiLSTM, CNN, LSTM, MLP, and large language models: BERT and GPT-2. This research systematically categorizes these features and evaluates their effectiveness, addressing the inherent challenges posed by the limited availability of Urdu-specific language models. Two distinct experiments were conducted to evaluate the impact of the proposed features on classification accuracy. In experiment one, the entire dataset was utilized for classification into intrinsic plagiarized and non-plagiarized documents. Experiment two categorized the dataset into three types based on topics: moral lessons, national celebrities, and national events. Both experiments are thoroughly evaluated through, a fivefold cross-validation analysis. The results show that the random forest classifier achieved an ex

关键词： Deep learning

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Generative AI for Energy Harvesting Internet of Things Network: Fundamental, Applications, and Opportunities

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IEEE Internet of Things Magazine 2025年第3期8卷 72-80页

作者： Xie, Wenwen Sun, Geng Li, Jiahui Wang, Jiacheng Du, Hongyang Niyato, Dusit Dobre, Octavia A. Jilin University China The College of Computing and Data Science Nanyang Technological University Singapore The University of Hong Kong China Memorial University The Faculty of Engineering and Applied Science Canada

Internet of Things (IoT) devices are typically powered by small-sized batteries with limited energy storage capacity, requiring regular replacement or recharging. To reduce costs and maintain connectivity in IoT networks, energy harvesting technologies are regarded as a promising solution. Notably, due to its robust analytical and generative capabilities, generative artificial intelligence (GenAl) has demonstrated significant potential in optimizing energy harvesting networks. Therefore, we discuss key applications of GenAl in improving energy harvesting IoT networks in this article. Specifically, we first review the key technologies of GenAI and the architecture of energy harvesting IoT networks. Then, we show how GenAI can address different problems to improve the performance of the energy harvesting IoT networks. Subsequently, we present a case study of unmanned aerial vehicle (UAV)-enabled data col-lection and energy transfer. The case study shows distinctively the necessity of energy harvesting technology and verify the effectiveness of GenAI-based methods. Finally, we discuss some import-ant open directions. © 2018 IEEE.

关键词： Costs Generative AI Reviews Energy exchange data collection Autonomous aerial vehicles Batteries Energy harvesting Internet of Things Battery management systems Energy storage

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MSNet: Momentum Spillover Network for Indian Stock Market Movement Prediction 20

MSNet: Momentum Spillover Network for Indian Stock Market Mo...

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20th IEEE India Council International Conference, INDICON 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)9798350305593

Investors and financial analysts require accurate predictions of stock prices to make informed decisions. We present Momentum Spillover Network (MSNet), a deep learning model that predicts stock prices on the National Stock Exchange of India by considering interdependencies existing between stocks in the same sector. Traditional approaches have focused solely on capturing hidden temporal dependencies, ignoring the impact of interconnectedness between stocks. To evaluate MSNet, we experimented with data from companies with the largest market capitalization from various sectors that covers over 70% of the NIFTY-50 index. MSNet effectively identifies the most impactful peer companies and predicts the future price movement of a target company with higher accuracy than existing deep learning approaches, highlighting the importance of incorporating relational dependency between stocks along with temporal dependency. © 2023 IEEE.

关键词： Deep Learning Momentum Spillover Effect Relational Modeling Stock Market Forecasting Temporal Modeling

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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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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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Pathfinding with Dynamic Obstacles

Pathfinding with Dynamic Obstacles

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Intelligent and Fuzzy Systems - Intelligence and Sustainable Future Proceedings of the INFUS 2023 Conference

作者： Patil, Anshul Kamboj, Shreshtha Bajaj, Anu Abraham, Ajith Thapar Institute of Engineering and Technology Patiala India Faculty of Computing and Data Sciences Flame University Pune India

ISBN: (纸本)9783031397769

Pathfinding algorithms are often at the center of any AI movement system. It is required to provide agents mobility in developing a realistic artificial intelligence (AI) for real-world scenarios. Its main goal is to find the optimized path from source to destination by avoiding obstacles. Finding a path through a maze is a major issue as it can be complex due to maze size, number of obstacles and have multiple routes that may lead to dead end. This paper proposed a search algorithm that finds the shortest path with dynamically created obstacles between the source and destination. It uses the A star algorithm and Manhattan distance as heuristic measure. The experiment was performed on grids of 10 × 10 to 100 × 100 size with step size of 10 × 10 on different obstacles ranges. The algorithm’s overall performance is respectable, and it can determine the quickest route between any two points. The proposed algorithm will always return a path for less than 30% obstacles and in cases where feasible paths can be generated. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Artificial intelligence

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Software Test Suite Minimization Using Hybrid Metaheuristics

Software Test Suite Minimization Using Hybrid Metaheuristics

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Intelligent and Fuzzy Systems - Intelligence and Sustainable Future Proceedings of the INFUS 2023 Conference

作者： Bajaj, Anu Abraham, Ajith Sambyal, Nitigya Thapar Institute of Engineering and Technology Patiala India Faculty of Computing and Data Sciences Flame University Pune India

ISBN: (纸本)9783031397738

Software testing is a crucial requirement for software quality. Continuous integration and continuous development lead to the inevitable growth of test cases, resulting in potential redundancies. It is a laborious and time consuming task to perform exhaustive re-testing. Test case minimization is a regression testing technique that filters and removes redundant test cases to reduce time and cost. It is like the minimal set cover problem, which satisfies some constraints with limited resources. Optimization algorithms are possible candidates to solve such NP-hard problems. Therefore, this paper presents the application of the hybrid of Bat algorithm and differential evolution (BADE) which takes into account the interference from prey and companions for regression test suite reduction. Two improvements were introduced to this hybrid algorithm 1) by changing the intensity of the interference (IBADEc) and 2) use of elite bats to enhance the exploitation at early stages (IBADEe). The proposed algorithms have been compared with the existing nature-inspired algorithms. The experimental results show that the proposed hybrid algorithm IBADEe outperformed other algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Software testing

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Influence of different form of multimedia on motivation and topic perception in augmented reality learning experiences

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Multimedia Tools and Applications 2025年 1-28页

作者： Yap, Shook Chin Rahmat, Rahmita Wirza Ali, Siti Khadijah School of Computing and Data Science Xiamen University Malaysia Selangor Sepang Malaysia Faculty of Computer Science and Information Technology Universiti Putra Malaysia Selangor Serdang Malaysia

It has been widely proven that Augmented Reality (AR) brings numerous benefits in learning experiences, including enhancing learning outcomes and motivation. However, not many studies investigate how different forms of multimedia help in motivating students in AR learning experiences. Instead of examining whether AR can be used in education to motivate students, our research focused on investigating how and when AR learning experiences influence motivation when integrated with multimedia learning. A between-subjects 2 × 3 factorial design was employed in this study. The first factor was the type of AR (static-AR or animated-AR), and the second factor was the modality of the description (concurrent narration-text, text only, or narration only). Six experimental conditions were designed, each equipped with different versions of AR learning applications with various combinations of multimedia formats. One hundred and forty-four computer science and information technology undergraduate students (70 males and 74 females) were randomly assigned to one of the six experimental conditions. data collection was conducted with questionnaires, and data were analyzed using univariate analysis of variance (ANOVA). Results show that attention and satisfaction subscales of the ARCS motivational model can be enhanced with 3D animation implemented in AR. Descriptions delivered with concurrent narration and written text in AR are effective in enhancing motivation as well. Besides, participants’ perceived understanding and interest in learning the topic increased after AR learning experiences. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Contrastive Learning

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