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44th ACM/IEEE International Conference on Software engineering, ICSE 2024

作者： Rinard, Martin Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science CambridgeMA United States

Generative artificial intelligence systems such as large language models (LLMs) exhibit powerful capabilities that many see as the kind of flexible and adaptive intelligence that previously only humans could exhibit. ... 详细信息

ISBN: (纸本)9798400702174

关键词： Software engineering

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Optimized Neural Network for Automated Cardiovascular Disease Classification: Meta-heuristic Enabled Model

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SN computer science 2025年第1期6卷 1-14页

作者： Kusuma, S. Jothi, K.R. Department of Computer Science & Engineering (Data Science) Madanapalle Institute of Technology & Science Madanapalle India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Cardiovascular disease remains a major issue for mortality and morbidity, making accurate classification crucial. This paper introduces a novel heart disease classification model utilizing Electrocardiogram (ECG) signals. The model consists of two main steps: feature extraction using a refined Lion Algorithm with New Mutated evaluation (LA-NM) and classification through an ensemble approach combined with a Neural Network (NN). The proposed model significantly outperforms traditional classifiers. At 80% training data, it achieves accuracy improvements of 8.4% to 3.11% over Neural Network (NN), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and ensemble models. At 90% training data, it shows precision improvements ranging from 1.03% to 0.31% better than the comparison models and specificity improvements ranging from 13.92% to 0.52%. These results underscore the effectiveness of the model and suggest its potential for further development in personalized treatment recommendations based on patient history. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Ensemble models Feature extraction Heart disease classification Neural network Optimization

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Blockchain-based Efficient and Secure Peer-to-Peer Distributed IoT Network for Non-Trusting Device-to-Device Communication

Informatica (Slovenia)

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Informatica (Slovenia) 2023年第4期47卷 515-522页

作者： Sharma, Rajesh Kumar Pippal, Ravi Singh Department of Computer Science & Engineering RKDF University Bhopal India

The security and privacy issues in the Internet of Things (IoT) are a mandatory process and also a challenging task for researchers. Blockchain technology enhanced and motivated the recent security parameters, and it has been validating various technical sectors since its inception. In this paper, a peer-to-peer distributed IoT network is presented where non-trusting devices can interact with other devices without a trustworthy intermediary using the blockchain technique in automated verifiable mode. Major implementation issues for deploying blockchain in the IoT network are pointed out in this paper. The model presents a modern blockchain technique to surpass the traditional security system for efficient and secure IoT deployment under various conditions. Finally, to validate the signification of blockchain in the IoT network, the avalanche effect is calculated and compared with Triple-DES, AES, and Blowfish cryptographic algorithms for non-trusting device-to-device (D2D) communications and transactions. The result presents significant output changes in hash for the blockchain-IoT integrated model as compared to other cryptographic algorithms. Conclusively, blockchain in the IoT network can make a remarkable impact across various industrial and business applications. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Internet of things

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Predicting Student Academic Performance Using an Optimised Regression Machine Learning Model 9

Predicting Student Academic Performance Using an Optimised R...

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9th IEEE International Conference on Adaptive science and Technology, ICAST 2024

作者： Martey, Ezekiel Mensah Abdullah, Abubakar Appiah, Obed Department of Computer Science and Engineering Tarkwa Ghana Department of Computer Science and Informatics Sunyani Ghana

ISBN: (纸本)9798350385403

Machine learning has emerged as one transformative tool in predicting student academic performance. This study evaluates machine learning models regression in predicting student academic performance. It can be found optimised Support Vector Regression (SVR) model accurately predicts student academic performance using a benchmark dataset from Kaggle. The approach incorporated Recursive Feature Elimination (RFE) for optimal feature selection while key parameters of the SVR model are tuned through grid search optimisation to enhance model performance. The optimised SVR model achieves exceptional predictive accuracy with an R-squared value of 0.97, Mean Absolute Error of 2.98, Mean Squared Error of 13.28, Root Mean Squared Error of 3.64, Median Absolute Error of 2.65, and Mean Absolute Percentage Error of 0.23. Comparative analysis demonstrates the superior performance of optimised SVR model against Decision Tree, Random Forest, K-Nearest Neighbors, Bayesian Ridge, and Gradient Boosting models. Experimental results demonstrate the validity of robustness and reliability of our approach, offering valuable insights for educational institutions to develop targeted interventions and support systems. © 2024 IEEE.

关键词： Educational Data Mining Grid Search Optimization Machine Learning Recursive Feature Elimination Student Performance Prediction

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An Innovative Approach Using TKN-Cryptology for Identifying the Replay Assault

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computers, Materials & Continua 2024年第1期78卷 589-616页

作者： Syeda Wajiha Zahra Muhammad Nadeem Ali Arshad Saman Riaz Muhammad Abu Bakr Ashit Kumar Dutta Zaid Alzaid Badr Almutairi Sultan Almotairi Department of Computer Science Alhamd Islamic UniversityIslamabad44000Pakistan Department of Computer Science and Technology University of Science and Technology BeijingBeijing100083China Department of Computer Science National University of TechnologyIslamabad44000Pakistan Department of Electrical Engineering National University of TechnologyIslamabad44000Pakistan Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Saudi Arabia Department of Computer Science Faculty of Computer and Information SystemsIslamic University of MadinahMedinah42351Saudi Arabia Department of Information Technology College of Computer Sciences and Information Technology CollegeMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Natural and Applied Sciences Faculty of Community CollegeMajmaah UniversityMajmaah11952Saudi Arabia

Various organizations store data online rather than on physical *** the number of user’s data stored in cloud servers increases,the attack rate to access data from cloud servers also *** researchers worked on different algorithms to protect cloud data from replay *** of the papers used a technique that simultaneously detects a full-message and partial-message replay *** study presents the development of a TKN(Text,Key and Name)cryptographic algorithm aimed at protecting data from replay *** program employs distinct ways to encrypt plain text[P],a user-defined Key[K],and a Secret Code[N].The novelty of the TKN cryptographic algorithm is that the bit value of each text is linked to another value with the help of the proposed algorithm,and the length of the cipher text obtained is twice the length of the original *** the scenario that an attacker executes a replay attack on the cloud server,engages in cryptanalysis,or manipulates any data,it will result in automated modification of all associated values inside the *** mechanism has the benefit of enhancing the detectability of replay ***,the attacker cannot access data not included in any of the papers,regardless of how effective the attack strategy *** the end of paper,the proposed algorithm’s novelty will be compared with different algorithms,and it will be discussed how far the proposed algorithm is better than all other algorithms.

关键词： Replay attack malware message attack file encryption cryptology data security

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An Efficient Deep Learning based Hybrid Model for Image Caption Generation

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International Journal of Advanced computer science and Applications 2023年第3期14卷 231-237页

作者： Kaur, Mehzabeen Kaur, Harpreet Department of Computer Science and Engineering Punjabi University Patiala India

In the recent yeas, with the increase in the use of different social media platforms, image captioning approach play a major role in automatically describe the whole image into natural language sentence. Image captioning plays a significant role in computer-based society. Image captioning is the process of automatically generating the natural language textual description of the image using artificial intelligence techniques. computer vision and natural language processing are the key aspect of the image processing system. Convolutional Neural Network (CNN) is a part of computer vision and used object detection and feature extraction and on the other side Natural Language Processing (NLP) techniques help in generating the textual caption of the image. Generating suitable image description by machine is challenging task as it is based upon object detection, location and their semantic relationships in a human understandable language such as English. In this paper our aim to develop an encoder-decoder based hybrid image captioning approach using VGG16, ResNet50 and YOLO. VGG16 and ResNet50 are the pre-trained feature extraction model which are trained on millions of images. YOLO is used for real time object detection. It first extracts the image features using VGG16, ResNet50 and YOLO and concatenate the result in to single file. At last LSTM and BiGRU are used for textual description of the image. Proposed model is evaluated by using BLEU, METEOR and RUGE score © 2023, International Journal of Advanced computer science and *** Rights Reserved.

关键词： Object detection

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Role of Artificial Intelligence in Bioinformatics and Information Extraction Systems

Decision Making: Applications in Management and Engineering

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Decision Making: Applications in Management and engineering 2025年第1期8卷 401-418页

作者： Al-Safarini, Maram Y. Baashirah, Rania A. Computer Science Department Zarqa University Jordan Software Engineering Department University of Business & Technology Saudi Arabia

Artificial Intelligence (AI) is transforming numerous domains, including bioinformatics and information extraction systems, by advancing data processing capabilities, enhancing precision, and facilitating automation. The primary aim of this research is to examine the function of AI within the realms of bioinformatics and information extraction through a combination of quantitative and qualitative approaches. The quantitative component involved a sample of 152 participants, recognised as experts in bioinformatics and data science. The survey findings underscore the prevalent integration of AI in bioinformatics, particularly through the utilisation of reinforcement learning, neural networks, and natural language processing (NLP). Furthermore, AI substantially improves the analysis of biological data;however, it encounters challenges such as limited model interpretability and the lack of data standardisation. Notwithstanding these obstacles, AI-driven innovations in disease prognosis, personalised healthcare, and pharmaceutical development are anticipated to shape the future trajectory of bioinformatics. The qualitative findings, derived through thematic analysis encompassing core themes and sub-themes, reveal that AI significantly contributes to accelerating and refining information extraction processes. Technologies such as machine learning, NLP, and neural networks are instrumental in enhancing data processing efficiency. Nonetheless, issues such as inadequate data quality, elevated computational expenses, and the intricacy of AI models remain persistent. Looking ahead, AI is projected to integrate more effectively with big data infrastructures, enable real-time information extraction, and deliver increasingly tailored solutions. The study concludes with several policy recommendations to guide future implementation strategies. © 2025 Regional Association for Security and crisis management. All rights reserved.

关键词： Data integration

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Detection of Images for Video Based Spectroscopy by Using the Combination of Saliency Methods 4

Detection of Images for Video Based Spectroscopy by Using th...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Kumar, Kaushal Bhushan Agarwal, Bharat Computer Science & Engineering Department India

ISBN: (纸本)9798350364729

Depth perception affects 2D image brightness, color, texture, and motion. In addition to stereoscopic vision, depth range, displaying size, 3D visualization, naturalness, and visual comfort can reconstruct 3D depth information. These features do not affect 2D visual attention. Replicating human visual attention in 3D situations requires saliency prediction methods designed for 3D environments. Stereoscopic videos' visual contrasts and object movements make saliency detection difficult. Depth map data is essential for saliency detection. For practical use, stereoscopic data is better than 2D. As visual scenes get more complex, 2D data alone cannot identify significant items. Depth maps help distinguish related items in complex situations. We use spatiotemporal correlation and depth information refinement to overcome these problems in this saliency detection investigation. Comparative analytical analysis of a baseline spatiotemporal depth correction system for multimodal saliency detection frameworks supports the basic premise. We chose two more saliency detection models: a cutting-edge multimodal filter transformer-based approach and a two-stream hierarchical model. We then compared these models to the baseline. We found that the spatiotemporal saliency model is effective due to its higher mean absolute error (MAE) values. Saliency models show that this method reduces visual region estimation and object detection mistakes. The spatiotemporal depth correction strategy is proven necessary and successful by this method. © 2024 IEEE.

关键词： Stereo image processing

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Concentric Blocking Techniques for Improved Feature Extraction in Local Binary Pattern (LBP) Systems 9

Concentric Blocking Techniques for Improved Feature Extracti...

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9th IEEE International Conference on Adaptive science and Technology, ICAST 2024

作者： Appiah, Obed Martey, Ezekiel Mensah Department of Computer Science and Informatics Sunyani Ghana Department of Computer Science and Engineering Tarkwa Ghana

ISBN: (纸本)9798350385403

The domain of image analysis has seen rapid advancements, particularly feature extraction techniques, driven by the increasing demand for accurate and efficient results. Among these advancements, Local Binary Patterns (LBP) have gained prominence due to their simplicity and computational efficiency. However, traditional LBP methods often need help with image orientation and scale variations, common in real-world scenarios. To address these challenges, this study explores the potential of concentric blocking methods as an alternative to conventional grid-based blocking techniques. Concentric blocking, which organizes image data into circular or spiral patterns, is hypothesized to enhance the robustness and accuracy of LBP-based systems by better accommodating geometric transformations. The study uses multiple LBP variants to compare traditional grid-based blocking with concentric blocking techniques across various image analysis tasks, including face recognition and texture classification. Experimental results demonstrate that the proposed ring-centric technique significantly outperforms traditional methods in precision and computational efficiency. Precision values for the proposed Ring Centric technique reach up to 90.73% for the basic LBP and 88.47% for Neighboring LBP with Distance (NLBPD), indicating its superior performance in handling variations in orientation and scale. Additionally, the technique demonstrates lower running times, making it a highly efficient and accurate choice for image retrieval tasks. This study highlights the importance of image-blocking strategies in enhancing the performance of LBP-based systems and underscores the potential of concentric blocking methods in overcoming the limitations of traditional approaches. The findings contribute to developing more resilient and efficient image analysis systems, with significant implications for applications in computer vision, medical diagnostics, and security systems. © 2024 IEEE.

关键词： Local binary pattern

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A systematic literature review of visual feature learning: deep learning techniques, applications, challenges and future directions

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Multimedia Tools and Applications 2024年 1-58页

作者： Abdullahi, Mohammed Oyelade, Olaide Nathaniel Kana, Armand Florentin Donfack Bagiwa, Mustapha Aminu Abdullahi, Fatimah Binta Junaidu, Sahalu Balarabe Iliyasu, Ibrahim Ore-ofe, Ajayi Chiroma, Haruna Department of Computer Science Ahmadu Bello University Zaria Zaria Nigeria Department of Mechanical Engineering Ahmadu Bello University Zaria Nigeria Department of Computer Engineering Ahmadu Bello University Zaria Nigeria College of Computer Science and Engineering University of Hafr Al Batin Hafar Al-Batin31991 Saudi Arabia School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast Belfast United Kingdom

Visual Feature Learning (VFL) is a critical area of research in computer vision that involves the automatic extraction of features and patterns from images and videos. The applications of VFL are vast, including object detection and recognition, facial recognition, scene understanding, medical image analysis, and autonomous vehicles. In this paper, we propose to conduct extensive systematic literature review (SLR) on VFL based on deep learning algorithms. The paper conducted an SLR covering deep learning algorithms such as Convolutional Neural Networks (CNNs), Autoencoders, and Generative Adversarial Networks (GANs) including their variants. The review highlights the importance of VFL in computer vision and the limitations of traditional feature extraction techniques. Furthermore, it provides an in-depth analysis of the strengths and weaknesses of various deep learning algorithms for solving problems in VFL. The discussion of the applications of VFL provides an insight into the impact of VFL on various industries and domains. The review also analyzed the challenges faced by VFL, such as data scarcity and quality, overfitting, generalization, interpretability, and explainability. The discussion of future directions for VFL includes hybrid techniques, unsupervised feature learning, continual learning, attention-based models, and explainable AI. These techniques aim to address the challenges faced by VFL and improve the performance of the models. The systematic literature review concludes that VFL is a rapidly evolving field with the potential to transform many industries and domains. The review highlights the need for further research in VFL and emphasizes the importance of responsible use of VFL models in various applications. The review provides valuable insights for researchers and practitioners in the field of computer vision, who can use these insights to enhance their work and ensure the responsible use of VFL models. © The Author(s), under exclusive licence to

关键词： computer vision

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