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3rd IEEE International Conference on Communications, Information System and computer engineering, CISCE 2021

作者： Ying, RunKai Shou, Yuntao Liu, Chang School of Software Beijing University of Technology Faculty of Information Technology Beijing China School of Computer and Information Engineering Central South University of Forestry and Technology Hunan China School of Computer Science and Technology Huaqiao University Xiamen China

ISBN: (纸本)9780738112152

How to accurately predict the Dow Jones index is an important issue in quantitative finance. This paper designs an LSTM-AdaBoost(Long Short-Term Memory and Adaptive Boosting) prediction model based on the Dow Jones index's characteristics. The AdaBoost algorithm was used to integrate multiple LSTM weak classifiers. By iteratively updating the weights of each version and combining the strategy, the improved robust classifier was formed, and the prediction for the Dow Jones index was finally obtained. Experimental results have shown that the prediction results obtained by the LSTM-AdaBoost model have been significantly higher than those of the traditional classification model, with an average increase of 43.76 percent in R_square. The establishment of this model provides a theoretical basis for predicting the Dow Jones index and provides ideas for the intersection of finance and computer industries. © 2021 IEEE.

关键词： Adaptive boosting

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A Review of Algorithms’s Complexities on Different Valued Sorted and Unsorted Data

A Review of Algorithms’s Complexities on Different Valued S...

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IT and Industrial Technologies (ICIT), International Conference on

作者： Attia Shabbir Asad Majeed Mahnoor Iftikhar Raja Hashim Ali Usama Arshad Muhammad Zeeshan Shabbir Ali Zeeshan Ijaz Nisar Ali Ali Aftab Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany Department of Electrical Engineering University of Engineering & Technology (UET) Lahore Pakistan Faculty of Electronic Systems Engineering University of Regina Regina Canada

Sorting and searching are critical processes for effecttive data analysis. In this paper, we evaluate the performance of various sorting and searching algorithms and compare their time and space complexities on both sorted and unsorted data. The algorithms we analyzed include six common sorting algorithms (insertion, radix, bucket, merge, bubble, and quick sort) and three search algorithms (linear, binary, and jump search). The results of our study provide insights into the best algorithms to use for different input sizes and types of data. It was found that for small input sizes, all algorithms perform similarly, but for larger input sizes, insertion and radix sorts are better for time complexity while bubble sort is better for space complexity. Additionally, jump search outperformed linear and binary search algorithms in both time and space complexity. Besides, difference between time and space complexity of sorted and unsorted data was significant.

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Abstractive Text Summarization in Arabic-like Script using Multi-Encoder Architecture and Semantic Extraction Techniques

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IEEE Access 2025年 13卷 104977-104991页

作者： Fatima, Wajiha Rizvi, Syed Saqib Raza Ghazal, Taher M. Kharma, Qasem M. Ahmad, Munir Abbas, Sagheer Furqan, Muhammad Adnan, Khan Muhammad Capital University of Science & Technology Department of Computer Science Islamabad Pakistan Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Department of Networks and Cybersecurity Amman Jordan Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Software Engineering Department Amman Jordan National College of Business Administration and Economics Department of Computer Science Lahore Pakistan Korea University University College Seoul 02841 South Korea Prince Mohammad Bin Fahd University Computer Science Department Alkhobar Saudi Arabia Capital University of Science & Technology Department of Software Engineering Islamabad Pakistan Gachon University Faculty of Artificial Intelligence and Software Department of Software Seongnam-si 13120 South Korea

In the field of Natural Language Processing (NLP), the task of text summarization plays a vital role in understanding textual content and producing concise summaries. Text summarization approaches can be categorized as either extractive or abstractive, with the latter largely unexplored in the Arabic script languages. Previous research on abstractive summarization for the Urdu language has progressed in creating logical and succinct summaries but has failed to preserve the underlying semantics, often resulting in inconsistent and ambiguous outputs. This study focuses on designing and implementing an abstractive text summarization model for the Urdu language by achieving the semantic meaning of the original text. The proposed model improves readability and comprehension by generating concise and meaningful overviews of original content while introducing new words and expressions. The study implements a multi-layer transformer encoder as part of the mBART (Multilingual BART) model. The encoder consists of multiple stacked transformer layers. It utilizes self-attention mechanisms to process and understand the input sequences effectively, achieving a BERTScore of 90%, a BLEU score of 43%, and ROUGE scores of 80.3% for ROUGE-1, 74.3% for ROUGE-2, and 80.3% for ROUGE-L. These results demonstrate the efficacy of the model in producing coherent and high-quality abstractive summaries for the Urdu language. © 2013 IEEE.

关键词： Abstractive Text Summarization (ATS) Natural Language Processing (NLP) Semantic Meaning Text Generation Text Summarization (TS)

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Using transformers and Bi-LSTM with sentence embeddings for prediction of openness human personality trait

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PeerJ computer Science 2025年 11卷 1-42页

作者： Naz, Anam Khan, Hikmat Ullah Alsahfi, Tariq Alhajlah, Mousa Alshemaimri, Bader Daud, Ali Department of Information Technology University of Sargodha Punjab Sargodha Pakistan Department of Information Systems and Technology College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia Applied of Computer Science College King Saud University Riyadh Saudi Arabia Software Engineering Department College of Computing and Information Sciences King Saud University Riyadh Saudi Arabia Faculty of Resilience Rabdan Academy Abu Dhabi United Arab Emirates

Understanding human personality traits is significant as it helps in decision making related to consumers’ behavior, career counselling, team building and top candidates’ selection for recruitment. Among various traits, openness is essential as it shows both diverse aspects of sensitive nature or intuitive nature. The individuals having a sensing nature tends to be more practical and prefer to focus on concrete information whereas the users having intuitive trait type is characterized by a focus on abstract ideas, creative thinking and future-oriented perspectives. In this research work, we aim to explore diverse natural language processing (NLP) based features and apply state of the art deep learning algorithms for openness trait prediction. Using standard Myers-Briggs Type Indicator (MBTI) dataset, we propose the use of the latest deep features of sentence embeddings which captures contextual semantics of the content to be used with deep learning models. For comparison, we explore textual features of Frequency-Inverse Document (TF-IDF) and parts of speech (POS) tagging with machine learning models and deep features of word2vec and global vectors for word representation (GloVe) with deep learning models. The comprehensive empirical analysis reveals that TF-IDF used with gradient boosting achieves high accuracy of 90% whereas, the deep feature of sentence embeddings when used and with deep model bidirectional long short-term memory (Bi-LSTM) achieves 90.5% accuracy. The best results have been achieved using the latest Transformer-based DistilBERT, which achieves the highest accuracy of 92% outperforming the existing studies in relevant literature. Subjects Human-computer Interaction, Artificial Intelligence, Data Mining and Machine Learning, Natural Language and Speech © 2025 xxxx

关键词： Deep learning

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Air Pollutant (CO) Forecasting: A Hybrid Approach Using Gooseneck Barnacle Optimization and Artificial Neural Networks

Air Pollutant (CO) Forecasting: A Hybrid Approach Using Goos...

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AI in Cybersecurity (ICAIC), International Conference on

作者： Marzia Ahmed Ahmad Johari Mohamad Md Maruf Hassan Touhid Bhuiyan Department of Software Engineering Daffodil International University (DIU) Dhaka Bangladesh Faculty of Electrical & Electronics Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) Malaysia Department of Computer Science and Engineering Southeast University Dhaka Bangladesh School of IT Washington University of Science and Technology (WUST) Virginia USA

ISBN: (数字)9798331518882

ISBN: (纸本)9798331518899

Air quality forecasting is critical for environmental monitoring and public health, and in this study, we propose a hybrid approach utilizing Gooseneck Barnacle Optimization (GBO) and Artificial Neural Networks (ANN) to enhance predictive accuracy. The air quality dataset, comprising pollutant concentrations (CO, NOx, benzene) and meteorological factors (temperature, humidity), is sourced from the UCI Machine Learning Repository. GBO is employed to fine-tune the ANN’s hyperparameters (especially the weight and bias), as well as the number of layers, neurons, and activation functions, to improve regression-based forecasting. The GBOANN model achieves a Mean Squared Error (MSE) of 0.562 and a Root Mean Squared Percentage Error (RMSPE) of 5.63%, outperforming other metaheuristic methods like BMO, SSA, DA, MVO, and EMA. This study demonstrates the GBO-ANN model’s potential for accurate real-time air quality forecasting, offering a robust, sustainable solution.

关键词： Accuracy Atmospheric modeling Artificial neural networks Predictive models Air quality Data models Real-time systems Environmental monitoring Forecasting Public healthcare

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A Secured and Optimized Clustering Approch in Vehicular Ad-hoc Network

A Secured and Optimized Clustering Approch in Vehicular Ad-h...

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Electrical, computer and Energy Technologies (ICECET), International Conference on

作者： Imran Khan Fazlullah Khan Asad Ali Bandar Alshawi Ryan Alturki Mohammad Wedyan Computer Science Department Abdul Wali Khan University Mardan KPK Pakistan Faculty of Science and Engineering School of Computer Science University of Nottingham Ningbo China Zhejiang China Department of Computer Science University of Engineering and Technology Peshawar KPK Pakistan Department of Computer and Network Engineering College of Computing Umm Al-Qura University Makkah Saudi Arabia Department of Software Engineering College of Computing Umm Al-Qura University Makkah Saudi Arabia Department of Computer Sciences Faculty of Information Technology and Computer Sciences Yarmouk University (YU) Irbid Jordan

ISBN: (数字)9798350395914

ISBN: (纸本)9798350395921

Vehicular ad hoc networks (VANETs) take vital role in intelligent transportation systems, but they face challenges due to dynamic network topology, impacting communication efficiency, especially with increasing active nodes. Security and privacy concerns are paramount, necessitating authentication to ensure only legitimate vehicles access the network. Secure vehicular clustering offers a solution, yet the number of clusters affects network dependability and efficiency. To tackle this, we present a secure clustering mechanism using Bat optimization algorithm, mimicking echolocation behavior to optimize clusters. We introduce an authentication technique for cluster heads to admit approved members, enhancing vehicular authentication. Simulations using MATLAB evaluate our approach, showing improved performance in end-to-end delay, overhead, and cluster head count compared to similar methods.

关键词： Privacy Network topology Authentication Vehicular ad hoc networks Security Vehicle dynamics Optimization Intelligent transportation systems Faces Matlab

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Exploring Multi-Perspective Document Structures for News Discourse Profiling

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 2349-2359页

作者： Hang Yang Dianbo Sui Yubo Chen Kang Liu Jun Zhao School of Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China School of Computer Science and Software Engineering Harbin Institute of Technology Weihai China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Key Laboratory of Cognition and Decision Intelligence for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Learning the discourse structures of news articles is vital for obtaining valuable structural information around main events, which has been shown useful for many natural language processing applications. This paper proposes the extraction of graphs explicitly representing the discourse structure, coreference structure, and semantic similarities within a document. Concept embeddings are then updated using a weighted combination of feature embeddings, modeled after capsule networks, and resulting in improved context-dependent representation of the content of each news article. Experimental tests on the NewsDiscourse dataset demonstrate the effectiveness of our proposed method.

关键词： Speech processing Routing Computational modeling Weapons Encoding Transformers Representation learning Natural language processing Context modeling Artificial intelligence

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Identity-based parallel key-insulated proxy signature in the random oracle model

Identity-based parallel key-insulated proxy signature in the...

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2020 International Conference on E-Commerce and Internet technology, ECIT 2020

作者： Chen, Jianhong Faculty of Computer and Software Engineering HuaiyinInstitute of Technology Huaian China

ISBN: (纸本)9781728159027

Exposure of private keys is harmful to the public key cryptosystems. In this paper, an identity-based parallel key-insulated proxy signature (IBPKIPS) scheme is given to solve the problem of proxy signing key exposure in the identity-based proxy signature system. The IBPKIPS scheme is secure in the random oracle model. Two different helper keys are used to refresh the temporary proxy signing keys in the scheme. The proposed scheme is more flexible than the IBPKIPS scheme in the standard model. © 2020 IEEE.

关键词： Authentication

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Next-Generation Human Activity Recognition Using Locality Constrained Linear Coding Combined With Machine Learning (NG-HAR-LCML)

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Healthcare technology Letters 2025年第1期12卷

作者： Shabbir, Maryam Ahmad, Fahad Alanazi, Saad Awadh Khan, Muhammad Hassan Li, Jianqiang Mahmood, Tariq Naseem, Shahid Anwar, Muhammad Department of Computer Sciences Bahria University Punjab Lahore Pakistan Department of Computer Science Faculty of Computing & Information Technology University of the Punjab Lahore Pakistan School of Computing Faculty of Technology University of Portsmouth Portsmouth United Kingdom University of Portsmouth Portsmouth United Kingdom Department of Computer Science College of Computer and Information Sciences Jouf University Aljouf Sakaka Saudi Arabia The School of Software Engineering Beijing University of Technology Beijing China Lab CCIS Prince Sultan University Riyadh Saudi Arabia Department of Information Sciences University of Education Vehari Pakistan Faculty of Information Sciences Division Science &Technology University of Education Lahore Pakistan

Accurate Human Activity Recognition (HAR) is a critical challenge with wide-ranging applications in healthcare, assistive technologies, and human-computer interaction. Traditional feature extraction methods often struggle to capture the complex spatial and temporal dynamics of human movements, leading to suboptimal classification performance. To address this limitation, this study introduces a novel encoding approach using Locality-Constrained Linear Coding (LLC) to enhance the discriminative power of hand-crafted features extracted from low-cost wearable sensors—an accelerometer and a gyroscope. The proposed LLC-based encoding scheme enables robust feature representation, improving the accuracy of HAR models. The encoded features are classified using a diverse set of Machine Learning (ML) and Deep Learning (DL) algorithms, including Support Vector Machine (SVM), Logistic Regression (LR), Naive Bayes (NB), K-Nearest Neighbours (KNN), AdaBoost, Gradient Boosting Machine (GBM), and Deep Belief Network (DBN). Extensive quantitative evaluations demonstrate that LLC significantly outperforms conventional feature encoding techniques, leading to improved classification accuracy. Among the tested models, DBN achieves a state-of-the-art accuracy of 99%, highlighting its superiority for HAR tasks. The contributions of this research are threefold: (1) it establishes the necessity of an advanced encoding scheme (LLC) for feature enhancement in HAR, (2) it provides a rigorous comparative analysis of multiple ML and DL classifiers, and (3) it introduces a scalable and cost-effective HAR framework suitable for real-world applications. Performance is comprehensively assessed using robust evaluation metrics such as Area Under the Curve (AUC), Classification Accuracy (CA), F1 Score, Precision, Recall, and Matthews Correlation Coefficient (MCC). The findings of this study offer new insights into feature encoding for HAR, setting a foundation for future advancements in sensor-based act

关键词： Support vector machines

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Entirety of Quantum Uncertainty and Its Experimental Verification

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Chinese Physics Letters 2021年第7期38卷 11-16页

作者： Jie Xie Li Zhou Aonan Zhang Huichao Xu Man-Hong Yung Ping Xu Nengkun Yu Lijian Zhang National Laboratory of Solid State Microstructures College of Engineering and Applied Sciences and School of PhysicsNanjing UniversityNanjing 210093China Collaborative Innovation Center of Advanced Microstructures Nanjing UniversityNanjing 210093China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China Shenzhen Institute for Quantum Science and Engineering and Department of Physics Southern University of Science and TechnologyShenzhen 518055China Shenzhen Key Laboratory of Quantum Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Institute for Quantum Information&State Key Laboratory of High Performance Computing College of ComputerNational University of Defense TechnologyChangsha 410073China Centre for Quantum Software and Information School of SoftwareFaculty of Engineering and Information TechnologyUniversity of Technology SydneyNSWAustralia

As a foundation of quantum physics,uncertainty relations describe ultimate limit for the measurement uncertainty of incompatible ***,uncertainty relations are formulated by mathematical bounds for a specific *** we present a method for geometrically characterizing uncertainty relations as an entire area of variances of the observables,ranging over all possible input *** find that for the pair of position and momentum operators,Heisenberg's uncertainty principle points exactly to the attainable area of the variances of position and ***,for finite-dimensional systems,we prove that the corresponding area is necessarily semialgebraic;in other words,this set can be represented via finite polynomial equations and inequalities,or any finite union of such *** particular,we give the analytical characterization of the areas of variances of(a)a pair of one-qubit observables and(b)a pair of projective observables for arbitrary dimension,and give the first experimental observation of such areas in a photonic system.

关键词： observable union operators

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