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Identify the potential pathways and candidate biomarkers of stroke associated with bipolar disorder: Bioinformatics and system biology approach

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Journal of Proteins and Proteomics 2025年第2期16卷 141-159页

作者： Hosen, Md. Faruk Basar, Md. Abul Yasmin, Mst. Farjana Hasan, Md. Rakibul Uddin, Muhammad Shahin Department of Information and Communication Technology (ICT) Mawlana Bhashani Science and Technology University (MBSTU) Tangail Bangladesh Department of Computing and Information System (CIS) Daffodil International University (DIU) Dhaka Bangladesh Department of Computer Science and Engineering (CSE) Uttara University Dhaka Bangladesh Department of Software Engineering (SWE) Daffodil International University (DIU) Dhaka Bangladesh

The field of computational bioinformatics and systems biology analysis is rapidly expanding due to advancements in bioinformatics tools. Bipolar disorder (BD) is a severe psychiatric condition affecting both adults and adolescents. In recent years, the risk of stroke has increased among individuals with BD. Background studies indicate that BD and stroke share numerous biochemical and genetic characteristics. Due to traditional endocrinological approaches, no collaborative research exists, and no treatment options have been established for BD and stroke patients. The aim of this study is to identify common molecular pathways and potential therapeutic targets in BD and stroke that could be utilized to predict disease progression. To achieve this, shared genes were identified to determine common pathways. Based on the biochemical, molecular, and genetic interactions among these shared genes, this study identifies the most significant hub genes. To further explore these interactions, analysis such as the protein–protein interaction (PPI) network, topological properties, enrichment analysis, co-expression network, gene regulatory network (GRN), and physical interaction network are conducted. The GO annotation indicates that most of the genes are linked to MAP kinase activity and tumor necrosis factor receptor superfamily binding. The KEGG pathway analysis revealed that the predominant genes are associated with apoptosis and the IL-18 signaling pathway. This comparison helps understand the biochemical and genetic characteristics shared between BD and stroke. Predictive drug analysis has identified consistent potential biomarkers associated with both conditions, providing a scientific foundation for investigating their diagnosis, treatment, and prognosis. Finally, chemical experiments could be conducted to further validate the efficacy of these drugs.

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Explicitly Diverse Visual Question Generation

SSRN

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SSRN 2024年

作者： Xie, Jiayuan Zheng, Jiasheng Fang, Wenhao Cai, Yi Li, Qing School of Software Engineering South China University of Technology Guangzhou China Ministry of Education China Department of Computing Hong Kong Polytechnic University Hong Kong Institute of Software Chinese Academy of Sciences Beijing China

Visual question generation involves the generation of meaningful questions about an image. Although we have made significant progress in automatically generating a single high-quality question related to an image, existing methods often ignore the diversity and interpretability of generated questions, which are important for various daily tasks that require clear question sources. In this paper, we propose an explicitly diverse visual question generation model that aims to generate diverse questions based on interpretable question sources. To explicitly perform question generation, our model first extracts the scene graph from the image using the unbiased scene graph generation method, where questions generated based on the scene graphs have interpretable question sources. To ensure the diversity of generated questions, our model selects different subgraphs from the scene graph as question sources. Specifically, we employ a subgraph selector to learn how humans select multiple subgraphs that are suitable for question generation. Finally, our model generates diverse questions based on different selected subgraphs. Extensive experiments on the VQA v2.0 and COCO-QA datasets show that the proposed model outperforms the baselines and is able to interpretably generate diverse questions. © 2024, The Authors. All rights reserved.

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Assessing Cybersecurity Awareness among the Hashemite University Students in Terms of Computer Usage 16

Assessing Cybersecurity Awareness among the Hashemite Univer...

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16th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2024

作者： Aljammal, Ashraf H. Qawasmeh, Ahmad Taamneh, Salah Wedyan, Fadi Obiedat, Mamoon Salameh, Hani Bani The Hashemite University Prince Al Hussein Bin Abdullah Ii Faculty of Information Technology Department of Computer Science and Applications Zarqa Jordan Lewis University Department of Engineering Computing and Mathematical Sciences RomeovilleIL United States The Hashemite University Prince Al Hussein Bin Abdullah Ii Faculty of Information Technology Department of Information Technology Zarqa Jordan The Hashemite University Prince Al Hussein Bin Abdullah Ii Faculty of Information Technology Department of Software Engineering Zarqa Jordan

ISBN: (纸本)9798350371154

Internet usage among information technology users has expanded substantially, as have cybercrimes with potentially disastrous repercussions. Therefore, internet users have to employ the available security measures and policies during internet usage. Nowadays, University students make up a sizable portion of internet users. This study assesses the cybersecurity awareness among The Hashemite University students In terms of Computer Usage. A questionnaire survey is used to assess students' cybersecurity knowledge while using their PCs and connecting to the internet, concentrating on their behaviors and use of security measures. © 2024 IEEE.

关键词： Cybersecurity

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Empirical Study on Mispronunciation Detection for Tajweed Rules during Quran Recitation

Empirical Study on Mispronunciation Detection for Tajweed Ru...

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computing and Informatics (ICCI), International Conference on

作者： Yousef S Alsahafi Muhammad Asad Department of Information technology Computing and Information technology at Khulis College University of Jeddah Jeddah Saudi Arabia Department of Computer and Software Engineering College of Electrical and Mechanical Engineering National University of Science and Technology Islamabad Pakistan

This paper presents an extensive empirical study aiming to identify the optimal combination of feature extraction techniques and machine learning algorithms, including deep learning, for automated mispronunciation detection during Quran recitation. Three feature extraction methods-Mel-Frequency Cepstral Coefficients (MFCC), Spectrogram, and Kaldi pitch - are systematically evaluated in conjunction with a diverse set of classifiers, including Gradient Boosting (GB), Support Vector Machines (SVM), Logistic Regression (LR), Random Forest (RF), Artificial Neural Network (ANN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks. The study leverages the QDAT dataset, comprising 1500 audio samples from 150 readers, with metadata specifying age, gender, and three distinct rules for mispronunciation detection. Experimental results demonstrate that the integration of MFCC with deep learning classifiers consistently achieves the highest performance across multiple evaluation metrics. Model robustness is validated using receiver operator characteristic curves (ROC) and accuracy metrics.

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Organizational governance: Resolving insufficient practice and quality expectation in Small software Companies. 22

Organizational governance: Resolving insufficient practice a...

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Proceedings of the 2022 European Symposium on software engineering

作者： Micheal Tuape Petrus Iiyambo Jussi Kasurinen Department of Software Engineering Lappeenranta-Lahti University of Technology Finland Dept of Computing Maths and Statistical Sciences University of Namibia Namibia

ISBN: (纸本)9781450397308

The quality of software products is among the most prevalent challenges threatening the software development primarily in small software companies (SSCs). These challenges are associated with insufficient practices affecting the production of software and the development processes. This paper explores the role of governance in streamlining software processes and practices to produce better quality software products. In a cross-sectional survey (n = 127), we reached out to software practitioners working in SSCs from four countries. We examined how SSCs engage in oversight and accountability and how SSCs perform management roles and activities, such as controlling, directing, and guiding in the process of developing software. Our findings indicate that although the SSCs minimally embrace governance practices, the smaller companies have a more challenging task embracing governance practices from the complexities arising out of these companies' structures. This study highlights the aspects of governance that need attention in the smaller category of SSCs. It proposes an organizational governance model to facilitate the SSCs in developing governance strategies to take advantage of the benefits of governance during software development.

关键词： software Organizational governance Small software Companies software processes

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Improved Fireworks Optimization Algorithm for Multi-product Human-robot Collaborative Hybrid Disassembly Line Balancing 4

Improved Fireworks Optimization Algorithm for Multi-product ...

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4th IEEE International Conference on Human-Machine Systems, ICHMS 2024

作者： Guo, Feng Guo, Xiwang Wang, Jiacun Wang, Weitian Cao, Jinrui Bigaume, Imane Liaoning Petrochemical University Information And Control Engineering Fushun China Monmouth University Computer Science And Software Eng. Wes Long BranchNJ United States Montclair State University School Of Computing Montclair United States New Jersey City University Computer Science Department Jersey City United States

ISBN: (纸本)9798350315790

In the remanufacturing process, the efficient disassembly of end-of-life (EOL) products assumes paramount significance for recycling endeavors. This work introduces a novel disassembly model aiming at enhancing disassembly efficiency within a hybrid disassembly line through the strengths of humanrobot collaboration. In order to optimize the generation of disassembly sequences, this work proposes an improved fireworks optimization algorithm (IFW) that combines uniform mutation and two-point crossover methods. Afterwards, the model's validity and algorithmic accuracy are substantiated using the CPLEX solver (a commercial solver). Furthermore, a comparative analysis and evaluation against alternative algorithms, such as salp swarm algorithm (SSA) and whale optimization algorithm (WOA), demonstrate a better stability of the proposed algorithm in the human-robot collaborative hybrid disassembly line balancing problem. © 2024 IEEE.

关键词： Optimization

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Schrödinger Mechanisms: Optimal Differential Privacy Mechanisms for Small Sensitivity

Schrödinger Mechanisms: Optimal Differential Privacy Mechan...

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IEEE International Symposium on Information Theory

作者： Wael Alghamdi Shahab Asoodeh Flavio P. Calmon Juan Felipe Gomez Oliver Kosut Lalitha Sankar School of Engineering and Applied Sciences Harvard University Department of Computing and Software McMaster University School of Electrical Computer and Energy Engineering Arizona State University

We consider the problem of designing optimal differential privacy mechanisms with a favorable privacy-utility tradeoff in the limit of a large number n of compositions (i.e., sequential queries). Here, utility is measured by the average distance between the mechanism's input and output, evaluated by a cost function c. We show that if n is sufficiently large and the sensitivities of all queries are small, then the optimal additive noise mechanism has probability density function fully characterized by the ground-state eigenfunction of the Schrödinger operator with potential c. This leads to a family of optimal mechanisms, dubbed the Schrödinger mechanisms, depending on the choice of the cost function. Instantiating this result, we demonstrate that for c(x) = x 2 the Gaussian mechanism is optimal, and for c(x) = |x|, the optimal mechanism is obtained by the Airy function, thereby leading to the Airy mechanism.

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Generative Adversarial Network-Based Network Intrusion Detection System for Supervisory Control and Data Acquisition System

Generative Adversarial Network-Based Network Intrusion Detec...

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2024 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2024

作者： Nguyen, Hong Nhung Lan-Phan, Thi Song, Chai-Jong School of Computing Gachon University Department of AI and Software Engineering Gyeonggi-do Seongnam-si1342 Korea Republic of Viet Nam - Korea Institute of Science and Technology Division of Information Technology Thach That Ha Noi Viet Nam Korea Electronics Technology Institute Information Media Research Center Seoul03924 Korea Republic of

ISBN: (纸本)9798331530839

In the context of burgeoning cyber threats targeting Supervisory Control and Data Acquisition (SCADA) systems, this paper presents a pioneering Intrusion Detection System (IDS) grounded in Generative Adversarial Networks (GANs). The ubiquity of information technology in critical industrial processes necessitates heightened security measures, and the proposed model addresses this imperative through the fusion of GANs and IDS tailored for SCADA environments. The study delves into the evolutionary trajectory of Intrusion Detection Systems, tracing their progression from conventional signature-based methods to the adaptive capabilities afforded by artificial intelligence, particularly deep learning. Against this backdrop, the paper investigates the vulnerabilities of SCADA systems, elucidating the sophisticated attack vectors identified in recent studies. The distributed nature of SCADA systems, deployed across extensive industrial landscapes, accentuates the need for robust intrusion detection mechanisms. This research's core innovation lies in applying a GAN model to SCADA systems, leveraging the distributed network protocol version 3 (DNP3) for data access authorization. The model is trained on a dataset comprising 12 meticulously selected features from DNP3 packets, attuning it to the nuances of SCADA network traffic. Notably, the proposed GAN-based IDS demonstrates exceptional efficacy, achieving impressive accuracy. Beyond the empirical contributions, the paper envisions future directions for integrating explainable AI techniques and enhancing the interpretability of IDS models specific to SCADA environments. Moreover, collaboration between academia and industry is advocated to foster the development of comprehensive datasets that represent SCADA network traffic. © 2024 IEEE.

关键词： Generative adversarial networks

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Classification and Segmentation of Mitotic Cells using Ant Colony Algorithm and TNM Classifier

Classification and Segmentation of Mitotic Cells using Ant C...

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2022 International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2022

作者： Vidhya, R.G. Sasikala, T.S. Ahamed, Ayoobkhan Mohamed Uvaze Khan, Subair Ali Liayakath Ali Singh, Kamlesh Saratha, M. HKBK College of Engineering Department of ECE Bangalore India Amrita College of Engineering and Technology Department of CSE Tamilnadu India Software Engineering New Uzbekistan University Tashkent Uzbekistan Westminster International University in Tashkent Computing Department Uzbekistan Graphic Era Hill University Department of CSE Dehradun India M.Kumarasamy College of Engineering Department of MCA Tamilnadu Karur India

ISBN: (纸本)9781665489621

Breast cancer develops from breast tissue and leads to abnormally growing cells in the chest. Doctors usually look for tumors on a mammogram, and some mammograms contain abnormal macrocalcifications and microcalcifications when the image quality is very poor. The presence of these abnormal amounts of calcium deposits in the breast is a sign of early breast cancer and should never be ignored. The image quality should be of the highest quality for an accurate interpretation of this mammographic deposit. Proposed research work is ongoing, exploring other screening methods and the stages of breast cancer. Improved Adaptive Fuzzy C-Means (IAFCM), Ant Colony Algorithm (ACA), and TNM (The size of the breast tumour (T), adjacent lymph nodes and Metastasized methods are used which builds the proposed medical image processing systems into an efficient way. Modified Poisson Inverse Gradient, Metastasized classifier (MPIG) has been used for classification. More than 500 image modalities are involved in all of the approaches. Clinical practitioners who make decisions based on photographs are predicted to benefit from the findings of this study. © 2022 IEEE.

关键词： Diseases

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SPUFChain: Permissioned Blockchain Lightweight Authentication Scheme for Supply Chain Management Using PUF of IoT

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IEEE Access 2025年 13卷 88662-88682页

作者： Assaqty, Mohammad Iqbal Saryuddin Gao, Ying Alqarni, Abeer D. Khan, Siraj Chakraborty, Mohuya Nur Siddiqi, Hafied Ahmad, Sadique South China University of Technology School of Computer Science and Engineering Guangzhou510006 China Universitas Nahdlatul Ulama Indonesia Faculty of Engineering and Computer Science Jakarta10320 Indonesia Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Technology P.O. Box 84428 Riyadh11671 Saudi Arabia Research Institute for Future Media Computing College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China University of Engineering and Management Cybersecurity Centre of Excellence Kolkata700160 India Riyadh12435 Saudi Arabia

The complexity of the entire process of supply chain management (SCM) is quite cumbersome and traditional way of handling it is devoid of proper authentication and security and very often suffers from human errors in dealing with flaws in quality control process of SCM. While it may have started with shipment tracking, the outcome of using IoT on supply chains has spread to every link in the chain. For instance, manufacturers are employing Internet-enabled sensors in production to find product faults, resulting in higher-quality production runs. Physical Unclonable Function (PUF) is a defense countermeasure that exploits the exclusive, unrepeatable features of hardware components to generate distinct cryptographic keys or identifiers, typically for a semiconductor device like an Internet of Things (IoT) device. The distinctive proof of identity of IoT devices in the supply chain is employed by means of PUFs as inviolable IDs. Blockchain, the distributed, immutable ledger, on the other hand is the disruptive technology that provides higher security as compared to traditional centralized systems. The integration of PUF and blockchain proves to be quite interesting while handling the above issues of authentication. A smart contract on the blockchain is a software code that executes spontaneously as and when the conditions of the contract or agreement are satisfied. Hence after authentication process the results are fed to blockchain smart contract for the final validation. This paper presents a novel permissioned blockchain smart contract-based lightweight authentication scheme for SCM using PUF of IoT known as SPUFChain. Informal and formal security analysis (using AVISPA and BAN logic) of the proposed framework show its efficiency to resist several threat scenarios like man-in-the middle, non-repudiation, impersonation, replay attacks and many other security features as compared to other related schemes. The processing time (~13.8ms) is better than existing lightweig

关键词： Supply chain management

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