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A Machine Learning-Based Multi-Model Approach for Predicting Post-Deep Brain Stimulation Outcomes in Alzheimer’s disease Patients

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Recent Patents on Engineering 2025年

作者： Thakur, Gopal Kumar Kulkarni, Shridhar Raguru, Jaya Krishna Tayubi, Iftikhar Aslam Rao, P Srinivasa Mudarakola, Lakshmi Prasad Reddy, P Chandra Shaker Department of Data Sciences Harrisburg University of Science & Technology Harrisburg PA United States Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science Faculty of Computing and Information Technology Rabigh (FCITR) King Abdulaziz University Jeddah Saudi Arabia School of CSE Department of ISE Jain University Karnataka Bengaluru India Computer Science and Engineering Institute of Aeronautical Engineering Telangana Hyderabad India School of Computer Science and Artificial Intelligence SR University Warangal India

Objectives and Background: Deep-brain stimulation (DBS) is a demonstrated rehabilitation for Alzheimer's Disease (AD), regularly resulting in an upgrade of engine work. In any case, some unfortunate incidental upshots can occur past DBS, which can deteriorate the patient's satisfaction. In this way, the medical group must painstakingly choose patients over whom to perform DBS. In the past, there have been a few endeavors to associate pre-usable information and DBS experimental results, generally centered on engine symptomatology. Objectives & Methodology: A machine learning (ML)-based strategy called a multimodel pipeline alluded to as FlowModel is designed and implemented to anticipate an enormous number of DBS medical results for AD. It is composed of a patented Artificial Neural Network (ANN) for processing medical data, using a conventional image handling technique to extract morphological biomarkers from T1 imaging, and employing a Support Vector Machine (SVM) to perform regressions. We authenticated it in 256 AD patients who underwent DBS. Results: The FlowModel demonstrated high predictive precision, achieving a correlation coefficient of as high as 0.82 and successfully predicting 63 out of 84 clinical outcome scores. Importantly, FlowModel outperformed traditional linear models, demonstrating the capability to learn effectively from preoperative data alone. Conclusion: The uniqueness of this study lies in its ability to predict multiple clinical outcomes across different modalities, independent of stimulation parameters. The introduction of this ML pipeline contributes to the precision medicine landscape by providing a robust, data-driven tool to select patients for DBS, potentially improving clinical decision-making and patient outcomes. © 2025 The Author(s). Published by Bentham science Publishers.

关键词： AD clinical prediction DBS machine learning neural networks support vector machine

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Blockchain based electronic educational document management with role-based access control using machine learning model

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Scientific Reports 2025年第1期15卷 1-19页

作者： P. Chinnasamy B. Subashini Ramesh Kumar Ayyasamy Ajmeera Kiran Binay Kumar Pandey Digvijay Pandey Mesfin Esayas Lelisho Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Engineering Srivilliputtur India Department of Data Science and Business Systems School of Computing SRMIST Kattankulathur Chennai India Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman Kampar Perak Malaysia Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Department of Information Technology College of Technology Govind Ballabh Pant University of Agriculture and Technology Pantnagar Uttrarakhand India Department of Technical Education (Government of Uttar Pradesh) Kanpur Uttar Pradesh India Department of Statistics College of Natural and Computational Science Mizan-Tepi University Tepi Ethiopia

The emergence of digital technology has led to a significant increase in the importance of educational credential storage, exchange, and verification for organisations, enterprises, and universities. Academic record forgery, record misuse, credential data tampering, time-consuming verification procedures, ownership and control difficulties, and other problems plague the education sector. Machine learning (ML) and blockchain, two of the most disruptive methods, have replaced traditional techniques in the education sector with highly technological and efficient ways. Our study aims to propose a novel electronic educational document management technique using a blockchain-based fuzzy feed-forward convolutional temporal neural network that detects malicious users. Here, the training is carried out based on NLP analysis in document word weight indexing. This document management access control is based on role-based access with simulated remora swarm optimisation. In order to identify malicious users, this suggested system logs access requests on the blockchain and authenticated users. The findings demonstrate that this suggested architecture performs as intended in every case. The experimental analysis is based on a malicious user detection dataset regarding Prediction accuracy, Mean average precision, F-measure, Latency, QoS, Contract execution time, and Throughput. Based on dataset feature analysis, the proposed B-FCTNN_SRSO achieved a prediction accuracy of 98%, a mean average precision (MAP) of 95%, and an F1 score of 97%, with a latency of 96%. Additionally, based on blockchain security analysis, the B-FCTNN_SRSO attained a QoS of 97%, a precision of 94%, and a throughput of 96%.

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Software Product Quality Models, Developments, Trends, and Evaluation

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SN Computer science 2020年第3期1卷 1-24页

作者： Galli, Tamas Chiclana, Francisco Siewe, Francois Institute of Artificial Intelligence (IAI) Faculty of Computing Engineering and Media De Montfort University Leicester United Kingdom Andalusian Research Institute on Data Science and Computational Intelligence (DaSCI) University of Granada Granada Spain Software Technology Research Laboratory (STRL) Faculty of Computing Engineering and Media De Montfort University Leicester United Kingdom

Software product quality models have evolved in their abilities to capture and describe the abstract notion of software quality since the 1970s. Many models constructed deal with a specific part of software quality only which makes them ineligible to assess the quality of software products as a whole. Former publications failed to thoroughly examine and list all the available models which attempt to describe each known property of software product quality. This paper discovers such complete software product quality models published since 2000;moreover, it endeavours to measure the relevance of each model quantitatively by introducing indicators with regard to the scientific and industrial community. The identified 23 software product quality model classes differ significantly in terms of publication intensity, publication range, quality score average, relevance score and the 12-month average of the Google Relative Search Index. The results offer a foundation for selecting the appropriate software product quality model for use or for extension if newly identified quality properties need to be connected to a general context. Furthermore, the experiences accumulated on the field of software product quality modelling motivated researchers to successfully transfer the concepts to other areas where abstract entities need to be compared or assessed including the quality of higher educational teaching and business processes, which is also briefly highlighted in the paper. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Execution tracing Quality assessment software engineering Software product quality model

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Multi-View Dynamic Kernelized Evidential Clustering

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IEEE/CAA Journal of Automatica Sinica 2024年第12期11卷 2435-2450页

作者： Jinyi Xu Zuowei Zhang Ze Lin Yixiang Chen Weiping Ding the Software Engineering Institute East China Normal UniversityShanghai 200062 Shanghai Key Laboratory of Trustworthy Computing East China Normal UniversityShanghai 200062China IEEE the School of Automation Northwestern Polytechnical UniversityXi’an 710072China the School of Artificial Intelligence and Computer Science Nantong UniversityNantong 226019 the Faculty of Data Science City University of MacaoMacao 999078China

It is challenging to cluster multi-view data in which the clusters have overlapping *** multi-view clustering methods often misclassify the indistinguishable objects in overlapping areas by forcing them into single clusters,increasing clustering *** solution,the multi-view dynamic kernelized evidential clustering method(MvDKE),addresses this by assigning these objects to meta-clusters,a union of several related singleton clusters,effectively capturing the local imprecision in overlapping *** offers two main advantages:firstly,it significantly reduces computational complexity through a dynamic framework for evidential clustering,and secondly,it adeptly handles non-spherical data using kernel techniques within its objective *** on various datasets confirm MvDKE's superior ability to accurately characterize the local imprecision in multi-view non-spherical data,achieving better efficiency and outperforming existing methods in overall performance.

关键词： Evidential clustering imprecision characterizing kernel technique multi-view clustering

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PVDM-YOLOv8l: a solution for reliable pedestrian and vehicle detection in autonomous vehicles under adverse weather conditions

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Multimedia Tools and Applications 2024年 1-26页

作者： Tahir, Noor Ul Ain Zhang, Zuping Asim, Muhammad Iftikhar, Sundas A. Abd El-Latif, Ahmed School of Computer Science and Engineering Central South University Changsha410083 China EIAS Data Science Lab College of Computer and Information Sciences and Center of Excellence in Quantum and Intelligent Computing Prince Sultan University Riyadh11586 Saudi Arabia Gianforte School of Computing Montana State University Bozeman59717 United States Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom32511 Egypt

Ensuring the safe navigation of autonomous vehicles in intelligent transportation system depends on their ability to detect pedestrians and vehicles. While transformer-based models for object detection have shown remarkable advancements, accurately identifying pedestrians and vehicles in adverse weather conditions remains a challenging task. Adverse weather introduces image quality degradation, leading to issues such as low contrast, reduced visibility, blurred edges, false detection, misdetection of tiny objects, and other impediments that further complicate the accuracy of detection. This paper introduces a novel Pedestrian and Vehicle Detection Model under adverse weather conditions, denoted as PVDM-YOLOv8l. In our proposed model, we first incorporate the Swin-Transformer method, which is designed for global extraction of feature of small objects to identify in poor visibility, into the YOLOv8l backbone structure. To enhance detection accuracy and address the impact of inaccurate features on recognition performance, CBAM is integrated between the neck and head networks of YOLOv8l, aiming to gather crucial information and obtain essential data. Finally, we adopted the loss function Wise-IOU v3. This function was implemented to mitigate the adverse effects of low-quality instances by minimizing negative gradients. Additionally, we enhanced and augmented the DAWN dataset and created a custom dataset, named DAWN2024, to cater to the specific requirements of our study. To verify the superiority of PVDM-YOLOV8l, its performance was compared against several commonly used object detectors, including YOLOv3, YOLOv3-tiny, YOLOv3-spp, YOLOv5, YOLOv6, and all the versions of YOLOv8 (n, m, s, l, and x) and some traditional models. The experimental results demonstrate that our proposed model achieved a 6.6%, 5.4%, 6%, and 5.1% improvement in precision, recall, F1-score and mean Average Precision (mAP) on the custom DAWN2024 dataset. This substantial improvement in accuracy ind

关键词： Vehicle detection

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Classification of Cancer Drug Compounds for Radiation Protection Optimization Using CART

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Procedia Computer science 2019年 161卷 458-465页

作者： Heri Kuswanto Rizky Mubarok Department of Statistics Faculty of Mathematics Computing and Data Science Kampus ITS Sukolilo Surabaya 60111 Indonesia

This paper applies Classification and Regression Tree (CART) to classify the selected compounds of cancer drugs related to radiation protection for cancer treatment. CART is one of machine learning methods that has been widely applied in drug design due to its simple algorithm and efficiency. The classification is applied to the 5%, 10%, 20%, 30%, 35% most important features selected by Mean Decreasing Gini Index. Moreover, the performance of CART on classification with full features is also investigated. The analysis shows that classification of cancer drug compounds using CART reached 79% accuracy when it uses 5% or 10% most important features. In this case, the performance of CART is slightly better than other complex machine learning methods applied in the previous researches.

关键词： CART toxicity machine learning drug discovery

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Evolution of GPU virtualization to resource pooling 2

Evolution of GPU virtualization to resource pooling

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2nd International Conference on Electronic Information Technology, EIT 2023

作者： Liang, Guicai Daud, Siti Norbaya Ismail, Nor Alina Binti Center for Outcome-Based Education Research Faculty of Engineering Built Environment & Information Technology SEGi University Kota Damansara Malaysia Faculty of Data Science and Computing University Malaysia Kelantan Kelantan Kota Bharu Malaysia

ISBN: (数字)9781510666627

ISBN: (纸本)9781510666610

With the rapid development of artificial intelligence, the demand for Graphics Processing Unit (GPU) resources is also increasing rapidly. To improve the efficiency and utilization of GPU resources, virtualization technology has been widely used in the field of GPUs. This article reviews the evolution from GPU virtualization to resource pooling, including device simulation, GPU pass-through, hardware-assisted virtualization, GPU full virtualization, GPU remote-sharing, and GPU resource pools. The advantages and disadvantages of each stage and the challenges faced during the evolution process are also analyzed. This article discusses the difficulties and solutions of GPU resource pool construction, as well as the construction steps and framework, providing a reference for the research and application of GPU resource pooling. © 2023 SPIE.

关键词： Graphics processing unit

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A hybrid approach for learning concept hierarchy from malay text using GAHC and immune network

A hybrid approach for learning concept hierarchy from malay ...

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8th International Conference on Artificial Immune Systems, ICARIS 2009

作者： Nazri, Mohd Zakree Ahmad Shamsuddin, Siti Mariyam Bakar, Azuraliza Abu Abdullah, Salwani Data Mining and Optimization Research Group Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Selangor 43650 Malaysia Soft Computing Research Group Faculty of Computer Science and Information System Universiti Teknologi Malaysia Skudai Johor 81100 Malaysia

ISBN: (纸本)3642032451

The human immune system provides inspiration in the attempt of solving the knowledge acquisition bottleneck in developing ontology for semantic web application. In this paper, we proposed an extension to the Guided Agglomerative Hierarchical Clustering (GAHC) method that uses an Artificial Immune Network (AIN) algorithm to improve the process of automatically building and expanding the concept hierarchy. A small collection of Malay text is used from three different domains which are IT, Biochemistry and Fiqh to test the effectiveness of the proposed approach and also by comparing it with GAHC. The proposed approach consists of three stages: pre-processing, concept hierarchy induction using GAHC and concept hierarchy learning using AIN. To validate our approach, the automatically learned concept hierarchy is compared to a reference ontology developed by human experts. Thus it can be concluded that the proposed approach has greater ability to be used in learning concept hierarchy. © 2009 Springer.

关键词： Ontology

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Analyze Decentralized Personal Health data using Solid, Digital Consent, and Federated Learning 14

Analyze Decentralized Personal Health Data using Solid, Digi...

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14th International Conference on Semantic Web Applications and Tools for Health Care and Life sciences, SWAT4HCLS 2023

作者： Sun, Chang van Soest, Johan Dumontier, Michel Institute of Data Science Faculty of Science and Engineering Maastricht University Maastricht Netherlands Department of Advanced Computing Sciences Faculty of Science and Engineering Maastricht University Maastricht Netherlands Brightlands Institute of Smart Society Faculty of Science and Engineering Maastricht University Maastricht Netherlands GROW School for Oncology and Reproduction Maastricht University Medical Centre Maastricht Netherlands

TIDAL is a Solid (SOcial Linked data)-based, citizen-centric data platform that facilitates interactions by citizens and researchers for health research. In this demonstration, we will show how TIDAL 1) can store personal data in Solid pods as RDF with well-known health-related vocabularies (e.g., SNOMED CT), 2) controls access to query fine-grained subsets of personal data, 3) enables researchers to post a human- and machine-readable digital consent using data Privacy Vocabulary, and 4) uses federated learning to analyze personal health data from multiple individuals using a Personal Health Train framework. TIDAL offers a new data paradigm of sharing and using personal data for research and ultimately increase the availability of personal data for societal relevant uses. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： data privacy

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An Interactive Application for University Students to Reduce the Industry-Academia Skill Gap in the Software Engineering Field

An Interactive Application for University Students to Reduce...

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Emerging Technology (INCET), International Conference for

作者： Salitha Tennakoon Raeesul Islam Danuja Wijerathna Rimzan Deen Prasanna Sumathipala Lakmini Abeywardhana Department of Data Science Faculty of Computing (SLIIT) Malabe Sri Lanka

Recent studies show that there is a big gap between industry and academia in the field of software engineering. This means that undergraduates often do not have the knowledge and skills that companies are looking for. When they first enter the workforce, undergraduates from our educational institutions frequently fall short of standards set by the industry, and the majority of these undergraduates are unskilled. To institutionalize them, the industry had to frequently mobilize additional resources to provide them with some internal education. The majority of these businesses inside industries have had to provide graduates with the knowledge and abilities necessary for them to perform their jobs well. Even though many computer science and software engineering courses mandate that students complete practical assignments, these are typically frivolous endeavors that fall far short of true professional growth. As a result, undergraduates frequently fall short of what employers are looking for and are turned down for interviews. This research paper identifies and investigates strategies for instructing university students in presentation, technical, and interview-facing skills to close this knowledge gap. One of the undergraduates’ biggest challenges is the lack of information and expertise required to work on a real industrial project. Some of the problems we discovered from our surveys included a lack of interviewing skills, a lack of presentational skills. The majority of undergraduates also have trouble locating reliable resources for learning new skills. Thus, our main goal is to develop a (UniHub) platform that overcomes the aforementioned problems and gives undergraduates virtual work experience so they may get ready for the workforce before they graduate.

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