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In silico prediction methods of self-interacting proteins:an empirical and academic survey

Frontiers of computer Science 2023年第3期17卷 199-216页

作者： Zhanheng CHEN Zhuhong YOU Qinhu ZHANG Zhenhao GUO Siguo WANG Yanbin WANG College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China School of Computer Science Northwestern Polytechnical UniversityXi’an 710129China Institute of Machine Learning and Systems Biology School of Electronics and Information EngineeringTongji UniversityShanghai 201804China School of Cyber Science and Technology Zhejiang UniversityHangzhou 310058China

In silico prediction of self-interacting proteins(SIPs)has become an important part of *** is an urgent need to develop effective and reliable prediction methods to overcome the disadvantage of high cost and labor intensive in traditional biological wet-lab *** goal of our survey is to sum up a comprehensive overview of the recent literature with the computational SIPs prediction,to provide important references for actual work in the *** this review,we first describe the data required for the task of DTIs ***,some interesting feature extraction methods and computational models are presented on this topic in a timely ***,an empirical comparison is performed to demonstrate the prediction performance of some classifiers under different feature extraction and encoding ***,we conclude and highlight potential methods for further enhancement of SIPs prediction performance as well as related research directions.

关键词： proteomics self-interacting proteins feature extraction prediction model

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Fast Implementation of Eta-T pairing on FPGAs

Fast Implementation of Eta-T pairing on FPGAs

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2024 International Conference on Horizons of Information Technology and engineering, HITE 2024

作者： Aslam, Waseem Raza, Asim University of Central Punjab Department of Software Engineering Lahore Pakistan University of Central Punjab Department of Computer Science Lahore Pakistan

ISBN: (纸本)9798331516055

Key distribution as a core feature of any security system is one of the challenging tasks in an online transaction. Pairing is used to share the key between the users as an answer to the underlying security problem. Diffie-Hellman key exchange protocol computes pairing in two rounds whereas Eta-T is the most efficient and widely used pairing algorithm in the field of elliptic curve cryptosystems. After careful analysis of existing Eta-T pairing techniques, this work proposes a modification from serial computation scheme to parallel for better overall performance. Parallel Karatsuba multiplier and Most Significant reduction algorithm is used to speed up and optimize the pairing algorithm. Consequently, proposed pairing accelerator calculates Eta-T pairing in just 36.4μs and takes only 3597 clock cycles with maximum clock frequency of 98.7 MHz on Virtex-II. Further, by increasing the number of coefficients processed per cycle up to 6, this work shows reduction in the calculation time and clock cycles of Eta-T pairing up to 26.16μs and 1997 respectively. It is also shown that calculation time starts increasing after reaching D = 14. Similarly, it is shown that the fastest calculation of Eta-T pairing in characteristics three over the field F397 uses significantly less clock cycles than previous known implementations at the cost of less clock frequency. © 2024 IEEE.

关键词： Electric clocks

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Optimizing learning outcomes: a deep dive into hybrid AI models for adaptive educational feedback

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Journal of Big Data 2025年第1期12卷 1-26页

作者： Qadir, Hafiz Muhammad Suleman, M. Taseer Khan, Rafaqat Alam Sohaib, Muhammad Hasan, Md Junayed Hussain, Syed Abid Department of Software Engineering Lahore Garrison University Lahore54810 Pakistan Department of Computer Science Bahria University Lahore Campus Lahore Pakistan School of Computer Science and Technology Zhejiang Normal University Jinhua321004 China Dataxense AberdeenAB11 5RG United Kingdom Department of Computer Science and Engineering Bakhtar University Kart-e-Char Kabul1001 Afghanistan

Accurate prediction of student performance is essential for the creation of adaptive learning frameworks and the best utilization of educational strategies. In this work, we apply ensemble learning and neural networks to investigate data from multiple sources about students, two real educational datasets from Kaggle, and two synthetically generated datasets. A Python-based generative script was used to create one synthetic dataset;another synthetic dataset is created by augmenting a smaller Kaggle dataset while keeping its original statistical distribution. The Integrated Synthetic Data will make the model more robust, mitigate class imbalance, and generalize predictively in a much better way across heterogeneous educational data. In this paper, we implement several ensemble models-AdaBoost, Gradient Boosting, XGBoost, LightGBM, and CatBoost-and deep learning architectures such as Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Recurrent Neural Networks (RNN). These models are evaluated using accuracy, precision, recall, F1-score, and ROC-AUC to assess their predictive effectiveness. Experimental results demonstrate that CatBoost outperforms other ensemble models with an accuracy of 0.7143 and an F1-score of 0.7338, while CNN achieves the highest performance for sequential data (accuracy: 0.6786). ROC-AUC analysis confirms CatBoost and XGBoost as top-performing classifiers, while CNN and DNN exhibit superior capability in handling temporal patterns. The study highlights the impact of dataset augmentation and synthetic data generation on improving predictive accuracy in educational data mining, reinforcing the importance of data-centric approaches for building intelligent, and evidence-driven educational systems. The learning feedback has been made available via a user-friendly webserver at: https://***/. © The Author(s) 2025.

关键词： Long short-term memory

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Next-Generation Cybersecurity Solution: A Decentralized Ransomware Recovery Network (DRRN) with Secret Sharing

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SN computer Science 2025年第5期6卷 1-22页

作者： Ali, Sijjad Wang, Jia Leung, Victor C. M. Ali, Asad College of Computer Science and Software Engineering Shenzhen University Yuehai Campus Guangdong Shenzhen 518060 China College of Electronics and information Engineering Shenzhen University Yuehai Campus Guangdong Shenzhen 518060 China

The increasing frequency of ransomware attacks, with a reported growth rate of 59%, has exposed significant vulnerabilities in existing cybersecurity measures. Traditional defenses, such as routine data backups and endpoint protection systems, often fall short against sophisticated ransomware that encrypts data and demands exorbitant payments for decryption keys. These limitations are exacerbated by attackers leveraging centralized storage systems and static recovery methods, making data recovery costly, unreliable, and time-consuming. To address this pressing issue, this paper proposes a novel Decentralized Ransomware Recovery Network, which leverages decentralized storage and secret sharing theory to enhance data security and recovery. The proposed scheme introduces a decentralized encryption/decryption framework that distributes data and keys across the network, reducing the economic incentive for attackers by enabling secure and reliable data recovery without ransom payments. At its core, the system utilizes Shamir’s Secret Sharing Scheme, where encryption keys are divided into shares and distributed among nodes. In the event of an attack, these shares are reconstructed to restore access to the encrypted files. Additionally, the proposed scheme incorporates a dynamic key reconfiguration mechanism, where keys are updated after each recovery process to thwart repeated attacks. Through extensive simulations and evaluations under various scenarios, the proposed scheme demonstrates superior performance in terms of recovery efficiency, security resilience, and adaptability, achieving a significant reduction in attack success rates. By offering a robust and preventative defense mechanism, this research highlights the novelty and practicality of the proposed scheme as an effective cybersecurity strategy against ransomware. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cybersecurity defense Decentralized Ransomware Recovery Network (DRRN) Ransomware Resilience Secret sharing schemes

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Stage-Specific Reinforcement Learning-Based Firewall for IoT Security Against Okiru Botnet 23

Stage-Specific Reinforcement Learning-Based Firewall for IoT...

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23rd International Conference on Cyberworlds, CW 2024

作者： Cho, Hyeon Seok Lee, Jeong Eun Oh, Sang Ho Pukyong National University Department of Computer Engineering & Artificial Intelligence Busan Korea Republic of Busan University of Foreign Studies Department of Computer Software Busan Korea Republic of

ISBN: (纸本)9798331527174

The emergence of sophisticated variant botnet attacks like Okiru poses significant security challenges to the proliferating IoT devices. This study proposes a reinforcement learning (RL)-based defense system for IoT networks. Our system employs an RL agent to analyze network packets in real-time and implement adaptive security measures. By leveraging AI techniques, this research offers a proactive approach to addressing complex cyber threats in IoT environments. © 2024 IEEE.

关键词： Bot (Internet)

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Automated Detection of COVID-19 in Chest Radiographs: Leveraging Machine Learning Approaches

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IEIE Transactions on Smart Processing and Computing 2024年第6期13卷 572-578页

作者： Batool, Raheela Raza, Ghulam Musa Khalid, Usman Kim, Byung-Seo Department of Business Administration Pusan National University Korea Republic of Department of Software & Communication Engineering Hongik University Sejong Korea Republic of Department of Computer Science Comsats University Islamabad Sahiwal Pakistan

The World Health Organization (WHO) has designated the COVID-19 pandemic a global health emergency, prompting responses all over the world. The fatality rate is between 2% and 5%, and millions of people around the world have been infected. While the WHO recommends tests, resource-intensive testing has motivated the development of CNN technology for automated identification. Research employing machine learning models shows great accuracy in classifying X-ray and CT images for COVID-19 detection. These models include denseNet201, resnet50V2, inceptionv3, mobile net, and custom CNNs. The interpretation of chest X-rays has come a long way, yet there are still obstacles to overcome. In this paper, we present a way for using a machine learning model to categorize chest X-ray pictures into normal, COVID-19, viral pneumonia, and lung opacity, demonstrating the model's efficacy in assisting medical diagnosis, especially in time-sensitive situations like COVID-19. Copyrights © 2024 The Institute of Electronics and Information Engineers.

关键词： COVID-19

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Discussion on the Influence of Anti-normalization Parameter Selection of BP Neural Network on Mending Seepage Pressure Data of Tailing Dam

Discussion on the Influence of Anti-normalization Parameter ...

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2022 International Joint Conference on Information and Communication engineering, JCICE 2022

作者： Zhu, Hongli Jin, Meiqi Wen, Yumeng Lu, Ling Liaoning University of Science and Technology School of Computer and Software Engineering Department of Software Engineering Liaoning Anshan China Liaoning University of Science and Technology School of Computer and Software Engineering Department of Network Engineering Liaoning Anshan China Liaoning University of Science and Technology School of Computer and Software Engineering Liaoning Anshan China

ISBN: (数字)9781665460675

ISBN: (纸本)9781665460675

The height of Infiltration line of tailings dam is a very important parameter for the safety of tailings reservoir. Infiltration line is the intersection line between the free water surface and the cross section of the dam body, that the free water surface is formed by the infiltration of water from the earth dam to the downstream. When the buried depth of the pressure sensors at each monitoring point could be obtained and the groundwater seepage pressure of the tailings dam could be measured too, the height of the free water surface can be calculated then. The commonly used pressure sensor is vibrating wire sensor. Among the many seepage pressure monitoring points, because of the lines, power supply or some other reasons, it often occurs that one or some points do not collect groundwater seepage pressure data for a period of time. For the missing data, BP neural network can be used to mend the missed data. Of course, the patched data should be marked to distinguish it from the real collected data. The mending method of missing seepage pressure data using BP neural network model is analyzed. The influence of anti-normalization parameters on mending data is analyzed. In the process of anti-normalization, the maximum and minimum values of the output data of the training data set are not put to use. Instead, a sequences is formed with the existing output data of some range before and after the mission data segment. The maximum and minimum values of the sequence are selected to use as the anti-normalization parameters. This will make the patched data values closer to the actual collected values. © 2022 IEEE.

关键词： Groundwater

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A Novel BNN-based Probabilistic Prediction Model for Non-linear and Non-stationary Time Series 4

A Novel BNN-based Probabilistic Prediction Model for Non-lin...

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4th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2022

作者： Feng, Kang Chen, Peng Gan, Zhihao Liu, Lei School of Computer and Software Engineering Xihua University China

ISBN: (纸本)9781450398343

Improving the prediction accuracy of time series is of great practical significance and theoretical value for people to prevent risks and supervise markets. However, due to the diversity of influencing factors of time series, time series often present nonlinear and non-correlated characteristics. This paper is committed to introducing nonlinear and non-stationary sequence processing methods through deep learning. First, the TCN (Temporal Convolutional Network) model, which is more suitable for sequence prediction, is selected from the basic model architecture. Then, construct BNN (Bayesian Neural Networks) to output the prediction distribution with confidence intervals. We chose two publicly available datasets for our study. Compared with N-BEAT, LSTM, and TCN methods, this paper has achieved higher prediction performance than the original methods. The distribution of confidence in the output enriches the output form and provides data support for subsequent research. © 2022 ACM.

关键词： Time series

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Liquid Neural Networks - Classification and Time Series Forecasting Use Case 8

Liquid Neural Networks - Classification and Time Series Fore...

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8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024

作者： Kavakli, Mehmet Ucar, Ferhat Gazi Vocational and Technical Collage Computer Science Department Elazig Turkey Firat University Department of Software Engineering Elazig Turkey

ISBN: (纸本)9798331531492

This study aims to comprehensively examine the potential of Liquid Neural Networks (LNNs) in machine learning field and various application areas. LNNs offer significant advantages over traditional neural networks due to their adaptive learning capacity and dynamic structures. The study involves visual classification using the MNIST dataset and time series analysis on the Yahoo Finance dataset. The performance of the LNN model on these two different datasets has been thoroughly evaluated through the training and testing processes, with results rigorously analyzed. This comprehensive analysis aims to provide an important discussion of the potential uses and advantages of the LNN model. © 2024 IEEE.

关键词： Time series

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Neural processor microarchitecture design for CNN processing acceleration 11

Neural processor microarchitecture design for CNN processing...

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Optoelectronic Imaging and Multimedia Technology XI 2024

作者： Tabunshchik, Sergei Bykovskii, Sergei Kustarev, Pavel Faculty of Software Engineering and Computer Systems ITMO University 49 Kronverksky Pr. St. Petersburg197101 Russia

ISBN: (纸本)9781510682061

Mixed reality systems that integrate the real and digital worlds have recently gained popularity. The development of mixed reality devices in the form of wearable devices has been a significant trend in recent years. Key challenges in designing wearable mixed reality devices include reducing power consumption, enhancing productivity, and minimizing the size of the device. To address these challenges, neural processors are being incorporated into mixed reality systems. These are specialized hardware accelerators for convolutional neural network (CNN) processing. The use of neural processors is due to the fact that most environmental analysis and visualizations in mixed reality rely on CNNs. The use of these energy-efficient, high-performance processing devices enables increased comfort in using the wearable device through the high-performance processing of environmental data on a compact device. The paper is devoted to the design of the microarchitecture of a neural processor for hardware acceleration of CNN processing based on the author's architecture of processor. The paper presents various microarchitectural solutions that can be used to accelerate CNN processing. We explore methods to optimize hardware resources and reduce time required for CNN processing. To achieve high throughput in pipelined computation, different algorithms for convolution calculations in a systolic array are examined. Based on the results of this research, we provide estimates of the characteristics of the neural processor with the proposed microarchitecture. © 2024 SPIE.

关键词： Convolutional neural networks

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