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Optimizing transcranial focused ultrasound parameters: A methodological advancement in non-invasive brain stimulation for next-gen clinical applications

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Neuroscience Informatics 2025年第2期5卷 100204-100204页

作者： Gupta, Sachin Mudhafar, Mustafa Borole, Yogini Dilip Mahalakshmi, V. Ramesh, Janjhyam Venkata Naga Khan, Muhammad Attique Department of CSE Maharaja Agrasen Institute of Technology Delhi India Department of Medical Physics Faculty of Medical Applied Sciences University of Kerbala Karbala 56001 Iraq Department of Anesthesia Techniques and Intensive Care Al-Taff University College Karbala 56001 Iraq Department of Mechatronics Engineering Marathwada Mitrmandals Institute of Technology Lohegaon Maharashtra Pune India Department of Computer Science College of Engineering and Computer Science Jazan University Jazan 45142 Saudi Arabia Department of CSE Graphic Era Hill University Dehradun Uttarakhand 248002 India Department of CSE Graphic Era Deemed To Be University Uttarakhand Dehradun 248002 India Center of AI Prince Mohammad bin Fahd University Saudi Arabia

Background: Transcranial-focused ultrasound (FUS), a non-invasive neuromodulation method, is gaining popularity for treating neurological and psychiatric disorders. However, changing stimulation settings for precise brain targeting remains challenging. Methods: Existing techniques have spatial resolution, skull acoustic transmission, and parameter selection issues that reduce clinical efficacy. These problems reduce tFUS application repeatability and safety. To address these challenges, this research proposes a novel computational-experimental strategy that combines advanced computational modeling (IACM) with in vivo validation. The proposed design uses subject-specific skull acoustic simulations, Deep Learning (DL)-based parameter optimization, and real-time feedback to increase stimulation accuracy and efficacy. Results: The recommended approach allows customized transcutaneous electrical nerve stimulation (tFUS) by modifying frequency, intensity, and targeting. Neuromodulation becomes better while staying safe. It should be adaptable enough for research and clinical usage to create neurostimulation precision medicine. Comparative analysis: The study shows that the proposed framework improves spatial precision, skull transmission effect variability, and neuromodulation efficacy compared to existing methods. Conclusion: This approach enables the development next-generation non-invasive brain stimulation devices with more therapeutic uses. Non-invasive brain stimulation (NIBS) technologies, including tFUS, TMS, and tDCS, may now accurately affect neurological and psychiatric diseases. However, these approaches are susceptible to inter-subject variability, poor targeting, and skull deformities. Artificial intelligence-driven real-time optimization frameworks like the Integrating Advanced Computational Modeling (IACM) framework are needed to overcome these constraints. © 2025 The Author(s)

关键词： Brain stimulation Computational modeling Non-invasive neuromodulation Optimization framework Precision medicine Transcranial focused ultrasound

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Identity Governance Framework for Privileged Users

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computer Systems Science & engineering 2022年第3期40卷 995-1005页

作者： Mansour Hammoud alruwies Shailendra Mishra Mohammed Abdul Rahman alShehri Department of Information Technology College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityMajmaah11952Saudi Arabia

Information technology companies have grown in size and recognized the need to protect their valuable *** a result,each IT application has its authentication mechanism,and an employee needs a username and *** the number of applications increased,as a result,it became increasingly complex to manage all identities like the number of usernames and passwords of an *** identities had to be retrieved by *** the identities and the access rights associated with those identities had to be protected by an *** couldn’t even capture such access rights because they couldn’t verify things like privacy and *** management can help solve this *** concept behind identity management is to centralize identity management and manage access identity centrally rather than multiple applications with their authentication and authorization *** this research work,we develop governance and an identity management framework for information and technology infrastructures with privileged access management,consisting of cybersecurity policies and *** results show the efficiency of the framework compared to the existing information security *** integrated identity and access management and privileged access management enable organizations to respond to incidents and facilitate *** can automate use cases that manage privileged accounts in the real world.

关键词： Privileged access management(PAM) lightweight directory access protocol(LDAP) identity management access control cyber-attack

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Corrigendum to “Remote sensing and machine learning approach for zoning of wastewater drainage system” [Desalin Water Treat 319 (2024) 100549]

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Desalination and Water Treatment 2025年 321卷

作者： A. Saranya alanoud al Mazroa Mashael Maashi T.M. Nithya V. Priya Department of Civil Engineering VOC College of Engineering Anna University Thoothukudi Campus Thoothukudi India Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University (PNU) P.O. Box 84428 Riyadh 11671 Saudi Arabia Department of Software Engineering College of Computer and Information Sciences King Saud University PO Box 103786 Riyadh 11543 Saudi Arabia Department of Computer Science and Engineering K.Ramakrishnan College of Engineering Trichy Tamil Nadu India Department of Civil Engineering GMR Institute of Technology Razam India

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A Deep Learning Approach for Prediction of Protein Secondary Structure

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computers, Materials & Continua 2022年第8期72卷 3705-3718页

作者： Muhammad Zubair Muhammad Kashif Hanif Eatedal alabdulkreem Yazeed Ghadi Muhammad Irfan Khan Muhammad Umer Sarwar Ayesha Hanif Department of Computer Science Government College UniversityFaisalabadPakistan Department of Computer Science College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Software Engineering/Computer Science Al Ain UniversityAbu DhabiUnited Arab Emirates

The secondary structure of a protein is critical for establishing a link between the protein primary and tertiary *** this reason,it is important to design methods for accurate protein secondary structure *** of the existing computational techniques for protein structural and functional prediction are based onmachine learning with *** deep learning architectures have already been applied to tackle protein secondary structure prediction *** this study,deep learning based models,i.e.,convolutional neural network and long short-term memory for protein secondary structure prediction were *** input to proposed models is amino acid sequences which were derived from CulledPDB *** tuning with cross validation was employed to attain best parameters for the proposed *** proposed models enables effective processing of amino acids and attain approximately 87.05%and 87.47%Q3 accuracy of protein secondary structure prediction for convolutional neural network and long short-term memory models,respectively.

关键词： Convolutional neural network machine learning protein secondary structure deep learning long short-term memory protein secondary structure prediction

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Optimizing process monitoring: Adaptive CUSUM control chart with hybrid score functions

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Measurement 2025年 254卷

作者： Babar Zaman Syed Zeeshan Mahfooz Naveed Khan Saddam Akber Abbasi Faculty of Engineering Sciences Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Khyber Pakhtunkhwa Pakistan Dept. of Computer Sc. and Engr. University of Hafar Al-Batin Saudi Arabia Department of Mechanical Engineering University of Hafr Al Batin Hafr Al Batin Saudi Arabia Department of Mathematics and Statistics College of Arts and Sciences Qatar University Qatar

The Cumulative Sum (CUSUM) Control Chart (CC) is a powerful tool for detecting small to moderate shifts in process parameters. While the traditional CUSUM CC determines the shift magnitude, it may not effectively handle a wide range of shifts encountered in real-world applications. To address this, an adaptive CUSUM (ACUSUM) CC has been developed. This study introduces a novel ACUSUM CC by integrating Huber and Bi-square score functions, which possess unique attributes such as monotonicity and redescending properties. Despite their individual advantages, limited research has explored their combined use in ACUSUM CC design. By amalgamating these functions into a unified formulation, we propose an innovative ACUSUM CC capable of effectively monitoring shifts of varying magnitudes in the process location parameter. The proposed CC is assessed through Monte Carlo simulations under both zero-state and steady-state conditions. Key performance metrics, including Average Run Length (ARL), median run length (MDRL), and Standard Deviation of Run Length (SDRL), are used to evaluate the ability of the proposed CC to detect individual shifts. Additionally, relative ARL, extra quadratic loss, and the process capability index are utilized to measure efficiency across a range of shifts, ensuring a comprehensive performance evaluation. A comparative analysis based on performance metrics and visual representations highlights the superior performance of the proposed CC over conventional methods. Furthermore, its application in energy usage analysis demonstrates its effectiveness in detecting anomalies, supporting proactive decision-making for optimizing energy consumption and resource allocation. These findings confirm the practicality, reliability, and enhanced monitoring capabilities of the proposed ACUSUM CC in real-world industrial and environmental scenarios.

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Exploiting Human Pose and Scene Information for Interaction Detection

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computers, Materials & Continua 2023年第3期74卷 5853-5870页

作者： Manahil Waheed Samia allaoua Chelloug Mohammad Shorfuzzaman Abdulmajeed alsufyani Ahmad Jalal Khaled alnowaiser Jeongmin Park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Engineering College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Korea

Identifying human actions and interactions finds its use in manyareas, such as security, surveillance, assisted living, patient monitoring, rehabilitation,sports, and e-learning. This wide range of applications has attractedmany researchers to this field. Inspired by the existing recognition systems,this paper proposes a new and efficient human-object interaction recognition(HOIR) model which is based on modeling human pose and scene featureinformation. There are different aspects involved in an interaction, includingthe humans, the objects, the various body parts of the human, and the backgroundscene. Themain objectives of this research include critically examiningthe importance of all these elements in determining the interaction, estimatinghuman pose through image foresting transform (IFT), and detecting the performedinteractions based on an optimizedmulti-feature vector. The proposedmethodology has six main phases. The first phase involves preprocessing theimages. During preprocessing stages, the videos are converted into imageframes. Then their contrast is adjusted, and noise is removed. In the secondphase, the human-object pair is detected and extracted from each image *** third phase involves the identification of key body parts of the detectedhumans using IFT. The fourth phase relates to three different kinds of featureextraction techniques. Then these features are combined and optimized duringthe fifth phase. The optimized vector is used to classify the interactions in thelast phase. TheMSRDaily Activity 3D dataset has been used to test this modeland to prove its efficiency. The proposed system obtains an average accuracyof 91.7% on this dataset.

关键词： Artificial intelligence daily activities human interactions human pose information image foresting transform scene feature information

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AMPCliff: Quantitative definition and benchmarking of activity cliffs in antimicrobial peptides

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Journal of Advanced Research 2025年

作者： Li, Kewei Wu, Yuqian Li, Yinheng Guo, Yutong Kong, Yanwen Wang, Yan Liang, Yiyang Fan, Yusi Huang, Lan Zhang, Ruochi Zhou, Fengfeng College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Jilin Changchun 130012 China School of Software Jilin University Jilin Changchun 130012 China School of Life Sciences Jilin University Jilin Changchun 130012 China Department of Computer Science Columbia University 116th and Broadway New York City 10027 NY United States School of Artificial Intelligence Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Jilin Changchun 130012 China School of Biology and Engineering Guizhou Medical University Guizhou Guiyang 550025 China School of Computer Engineering Changchun University of Engineering Jilin Changchun 130103 China Changchun Wenli High School Jilin Changchun 130062 China

Introduction: Activity cliff (AC) is a phenomenon that a pair of similar molecules differ by a small structural alternation but exhibit a large difference in their biochemical activities. This phenomenon affects various tasks ranging from virtual screening to lead optimization in drug development. The AC of small molecules has been extensively investigated but limited knowledge is accumulated about the AC phenomenon in pharmaceutical peptides with canonical amino acids. Objectives: This study introduces a quantitative definition and benchmarking framework AMPCliff for the AC phenomenon in antimicrobial peptides (AMPs) composed by canonical amino acids. Methods: This study establishes a benchmark dataset of paired AMPs in Staphylococcus aureus from the publicly available AMP dataset GRAMPA, and conducts a rigorous procedure to evaluate various AMP AC prediction models, including nine machine learning, four deep learning algorithms, four masked language models, and four generative language models. Results: A comprehensive analysis of the existing AMP dataset reveals a significant prevalence of AC within AMPs. AMPCliff quantifies the activities of AMPs by the metric minimum inhibitory concentration (MIC), and defines 0.9 as the minimum threshold for the normalized BLOSUM62 similarity score between a pair of aligned peptides with at least two-fold MIC changes. Our analysis reveals that these models are capable of detecting AMP AC events and the pre-trained protein language model ESM2 demonstrates superior performance across the evaluations. The predictive performance of AMP activity cliffs remains to be further improved, considering that ESM2 with 33 layers only achieves the Spearman correlation coefficient 0.4669 for the regression task of the −log(MIC) values on the benchmark dataset. Conclusion: Our findings highlight limitations in current deep learning-based representation models. To more accurately capture the properties of antimicrobial peptides (AMPs), it is ess

关键词： Activity cliff (AC) AMPCliff Antimicrobial peptide (AMP) Benchmark Minimum inhibitory concentration (MIC)

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Ambiguity learning and data correlation in multi-cross modal cyber-physical systems for detecting fake information

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Cyber-Physical Systems 2025年

作者： Byeon, Haewon alMahadin, Ghayth Bsoul, Qusay Quraishi, Aadam Soni, Mukesh Khan, Shahi Raza Shabaz, Mohammad Department of AI and Software Inje University Gimhae Korea Republic of Data Science Department College of Information Technology Mutah University Mutah Jordan Department of Cybersecurity College of Computer Sciences and Informatics Amman Arab University Amman Jordan M.D. Research Intervention Treatment Institute HoustonTX United States Dr. D. Y. Patil School of Science & Technology Dr. D. Y. Patil Vidyapeeth Pune Pune India MJMC Centre for Media and Mass Communication Jamia Hamdard Delhi India Model Institute of Engineering and Technology Jammu India

This paper proposes an improved cyber-physical false information detection model based on cross-modal ambiguous learning. This work introduced the IC2 LFD model with an emphasis on ‘text-image’ ambiguity, focusing on the main goal of intelligent multi-cross modal data fake information identification. The experiment revealed that the significance of text and images differs in intelligent multi-cross modal data fake information detection. The effectiveness of the model is verified on the twitter dataset, showing a 6% accuracy improvement compared to the baseline model and a 1.6% performance improvement over detection methods without dynamic weight allocation. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Adversarial machine learning

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Evaluation of Hotel Performance with Sentiment Analysis by Deep Learning Techniques

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2023年第9期17卷 70-87页

作者： Hameed, Rafeef A. Abed, Wael J. Sadiq, Ahmed T. Computer Sciences Department University of Technology Baghdad Iraq Computer Techniques Engineering Department Al-Mustaqbal University College Hillah Iraq

The subject of sentiment analysis through social media sites has witnessed significant development due to the increasing reliance of people on social media in advertising and marketing, especially after the Corona pandemic. There is no doubt that the prevalence of the Arabic language makes it considered one of the most important languages all over the world. Through human comments, it can know things if they are positive or negative. But in fact, the comments are many, and it takes work to evaluate the place or the product through a detailed reading of each comment. Therefore, this study applied deep learning approaches to this issue to provide final results that could be utilized to differentiate between the comments in the dataset. Arabic Sentiment Analysis was used and gave a percentage for each positive and negative commentary. This work used eight methods of deep learning techniques after using Fast Text as embedding, except Ara BERT. These techniques are the transformer (AraBERT), RNN (Long short-term memory (LSTM), Bidirectional long-short term memory (BILSTM), Gated recurrent units (GRUs), Bidirectional Gated recurrent units (BIGRU)), CNN (like alEXNET, proposed CNN), and ensemble model (CNN with BI-GRU). The Hotel Arabic Reviews Dataset was utilized to test the models. This paper obtained the following results. In the Ara BERT model, the accuracy is 96.442%. In CNN, like the alex Net model, the accuracy is 93.78%. In the suggested CNN model, the accuracy is 94.43%. In the suggested LSTM model, the accuracy is 95%. In the suggested BI-LSTM model, the accuracy is 95.11%. The accuracy of the suggested GRU model is 95.07%. The accuracy of the suggested BI-GRU model is 95.02%. The accuracy is 94.52% in the Ensemble CNN with BI-GRU model that has been proposed. Consequently, the AraBERT outperformed the other approaches in terms of accuracy. Because the AraBERT has already been trained on some Arabic Wikipedia entries. The LSTM, BI-LSTM, GRU, and BI-GRU, on the o

关键词： Sentiment analysis

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Improving the Ambient Intelligence Living Using Deep Learning Classifier

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computers, Materials & Continua 2022年第10期73卷 1037-1053页

作者： Yazeed Yasin Ghadi Mouazma Batool Munkhjargal Gochoo Suliman Aalsuhibany Tamara al Shloul Ahmad Jalal Jeongmin Park Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science and Software Engieering United Arab Emirates UniversityAl Ain15551UAE Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Humanities and Social Science Al Ain UniversityAl Ain15551UAE Department of Computer Engineering Tech University of KoreaGyeonggi-do15073Korea

Over the last decade,there is a surge of attention in establishing ambient assisted living(Aal)solutions to assist individuals live *** a social and economic perspective,the demographic shift toward an elderly population has brought new challenges to today’s *** can offer a variety of solutions for increasing people’s quality of life,allowing them to live healthier and more independently for *** this paper,we have proposed a novel Aal solution using a hybrid bidirectional long-term and short-term memory networks(BiLSTM)and convolutional neural network(CNN)*** first pre-processed the signal data,then used timefrequency features such as signal energy,signal variance,signal frequency,empirical mode,and empirical mode *** convolutional neural network-bidirectional long-term and short-term memory(CNN-biLSTM)classifier with dimensional reduction isomap algorithm was then used to select ideal *** assessed the performance of our proposed system on the publicly accessible human gait database(HuGaDB)benchmark dataset and achieved an accuracy rates of 93.95 percent,*** reveal that hybrid method gives more accuracy than single classifier in Aal *** suggested system can assists persons with impairments,assisting carers and medical personnel.

关键词： Ambient assisted living convolutional neural network dimensionality reduction frequency-time features wearable technology

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