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A Logical Evaluation of Predicting Immune System Strength by using Enhanced Deep Learning Strategy

A Logical Evaluation of Predicting Immune System Strength by...

2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Malathi, K. Ramkumar, M. Ruby, E. D. Kanmani Santhi, G.B. Korde, Vivek Aljawawdeh, Hamzeh J. Saveetha University Department of Cse Chennai India Veltech Rangarajan Dr Sagunthala R & D Institute of Science & Technology Department of Ece Chennai India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science and Engineering Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa University Faculty of information technology Department of software engineering Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

The immune system plays a crucial role in defending the body against diseases, making it vital to develop models that can accurately predict immune system strength. This study presents an Enhanced Deep Learning Strategy (EDLS) designed to predict immune system strength based on immune-related biomarkers such as White Blood Cell (WBC) count and Immunoglobulins. A dataset consisting of 23,654 records with 12 features was collected from clinical sources. The data underwent comprehensive preprocessing, including noise reduction using a Median Filter, feature selection with Random Forest, and data fusion using Kalman Filters. The EDLS model integrates AutoEncoder for dimensionality reduction and Cascaded Long Short-Term Memory (LSTM) for capturing temporal dependencies. The proposed model outperforms nine traditional and advanced machine learning models, including Logistic Regression, Support Vector Machine (SVM), and Convolutional Neural Network (CNN), with an accuracy of 94.72%. Evaluation metrics such as Precision (92.01%), Recall (93.77%), and F1-Score (92.88%) highlight the model's effectiveness in predicting immune system strength. Feature importance analysis reveals that WBC count and Immunoglobulins are the most influential predictors. This study demonstrates the potential of EDLS for enhancing immune strength prediction, which can have significant implications for personalized healthcare and disease prevention. © 2024 IEEE.

关键词： Median filters

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Probabilistic Graph Modeling based Safety Classifier Algorithm for Smart Transportation

Probabilistic Graph Modeling based Safety Classifier Algorit...

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2023 International Symposium on Green Technologies and Applications, ISGTA 2023

作者： Karkouri, Najib El Tigani, Smail Saadane, Rachid Chehri, Abdelah Pierre, Samuel Neya, Noureddine Mathematics and Informatics Lab Faculty of Sciences University Ibno Toufial Kenitra Morocco Research and Development Unit Accsellium LLC Fez Morocco SIRC-LaGeS Hassania School of Public Labors Casablanca20250 Morocco Department of Mathematics and Computer Science Royal Military College of Canada Kingston Canada Department of Computer and Software Engineering Polytechnique Montreal Canada

This research introduces a novel Probabilistic Graph Modeling-based Safety Classifier Algorithm designed for the purpose of classifying road safety in smart transportation systems. Leveraging a combination of numerical integration methods and Gaussian kernel density estimation models, the proposed algorithm offers an effective approach to assess and categorizing the safety levels of roads. The integration of these techniques enables a comprehensive analysis, allowing for a more nuanced understanding of the complex interactions within transportation networks. The algorithm's efficacy in road safety classification holds promising implications for enhancing traffic management and promoting safer urban environments. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Accident prevention

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Genetic Algorithm-Based Feature Selection for Accurate Breast Cancer Classification

Genetic Algorithm-Based Feature Selection for Accurate Breas...

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2023 International Conference on IT and Industrial Technologies, ICIT 2023

作者： Shabbir, Attia Ali, Raja Hashim Shabbir, Muhammad Zeeshan Abideen, Zain Ul Khan, Talha Ali Ijaz, Ali Zeeshan Ali, Nisar Imad, Muhammad Bakar, Muhammad Abu Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Department of Electrical Engineering Lahore Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350315974

Breast cancer is a significant global healthcare challenge, particularly in developing and underdeveloped countries, with profound physical, emotional, and psychological consequences, including mortality. Timely diagnosis and accurate treatment are crucial in addressing this issue. We propose the utilization of a feature selection technique to identify the most relevant features from among all features for breast cancer diagnosis, and show that Genetic Algorithms are impressive for this task. The study compares the results of GA with no selection and an alternative method, Principle Component Analysis (PCA). Three machine learning models, all based on supervised learning with data split into training and test data, are employed for binary classification using the selected feature subset. The evaluation metrics employed encompass accuracy, precision, recall, and F1-score. Among the selected models, Random Forest demonstrates the most favorable outcomes, achieving an accuracy score of 0.96, precision score of 0.96, recall value of 0.98, and an F1-score of 0.97. These results underscore the effectiveness of GA in feature selection for breast cancer diagnosis. Consequently, the integration of Genetic Algorithms (GA) with Random Forest showcases the superior performance among the evaluated models. © 2023 IEEE.

关键词： Genetic algorithms

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Comparison of Standard Ant Colony Optimization, Dijkstra and A* Algorithms for Path Planning of Robot in an Indoor Environment

Comparison of Standard Ant Colony Optimization, Dijkstra and...

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Mechanical engineering and Robotics Research (ICMERR), International Conference on

作者： Sudeep Sharan Anh Tong Ngoc Minh Juan José Domínguez-Jiménez Peter Nauth Faculty of Computer Science and Engineering Frankfurt University of Applied Sciences Frankfurt am Main Germany UCASE Software Engineering Group School of Engineering University of Cádiz Cadiz Spain

ISBN: (数字)9798331509262

ISBN: (纸本)9798331509279

The research topic of Path planning is extremely challenging area of concentration within the field of mobile robots. However, path planning algorithms for mobile robot tasks are contingent upon the environment and its level of complexity. This paper analyzes four distinct path planning methods for simulated indoor environment. The proposed algorithms include conventional Ant Colony methods, Ant System (AS), Ant Colony System (ACS), as well as standard methods Dijkstra and AStar (A*). We analyzed and examined these algorithms by employing various metrics/maps with complexity. The results indicate that the traditional path planning algorithm Dijkstra and A* approaches surpass the other ant colony techniques in terms of both computation time and path distance.

关键词： Ant colony optimization Heuristic algorithms Stars Path planning Real-time systems Indoor environment Complexity theory Mobile robots Standards Testing

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Enhanced Multi-Head Self-Attention Transformer-Based Method for Prediction of Drug-Drug Interactions

Enhanced Multi-Head Self-Attention Transformer-Based Method ...

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Semih Cal Ramazan Ozgur Dogan Hulya Dogan Department of Engineering Computer Science & Physics Otterbein University Westerville OH USA Department of Artificial Intelligence Engineering Faculty of Computer and Information Sciences Trabzon University Trabzon Türkiye Department of Software Engineering Faculty of Engineering Karadeniz Technical University Trabzon Türkiye

ISBN: (数字)9798331509934

ISBN: (纸本)9798331509941

The usage of various drugs has increased significantly in recent years, leading to a higher possibility of drug-drug interactions (DDIs). The concurrent usage of multiple drugs can result in potentially hazardous interactions, making it crucial to foresee DDIs to prevent adverse effects and enhance patient safety. Traditional DDI prediction methods often require extensive examinations, which can be time consuming and resource intensive. As a result, automatic DDI prediction methods have gained attention in the literature, offering clinicians support for making more accurate decisions and designing effective treatment plans. Despite progress in DDI prediction studies, substantial challenges remain in the field. Our study addresses these challenges by proposing a deep learning-based model leveraging drug features. Specifically, this study introduces an enhanced multi-head self-attention transformer-based method, which incorporates pharmacological features to achieve improved performance. The proposed method consists of two primary stages: feature extraction and model design. To evaluate the efficacy of the proposed method, performance evaluation procedures -Accuracy (ACC), Precision (PRE), Recall (REC), and F -Score-are utilized. Comparative experiments are conducted with several state-of-the-art methods on a data set specifically created for this study. Out of all, the proposed method achieves mean values of ACC, PRE, REC, and F-Score as 87.49%, 87.19%, 82.76%, and 84.56 %, respectively, surpassing the performance of other methods. The results unequivocally demonstrate the effectiveness and superiority of the proposed method in predicting DDIs.

关键词： Drugs Learning systems Accuracy Predictive models Feature extraction Transformers Robustness Safety Matrix decomposition Security

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Intelligent-Based Extraction of Business Capabilities from Company Descriptions

Intelligent-Based Extraction of Business Capabilities from C...

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International Conference on Information Technology, Information Systems and Electrical engineering (ICITISEE)

作者： Faisal Fahmi Siti Hafizah Ab Hamid Imam Yuadi Department of Library and Information Science Faculty of Political and Social Science Airlangga University Department of Software Engineering Faculty of Computer Science & Information Technology Universiti Malaya

In the era of Big Data, Information can be generated, extracted, and utilized in diverse ways. In business, information about business capabilities can be a crucial aspect in understanding the strengths and competencies of organizations. This Information can empower stakeholders to make informed decisions regarding partnerships, acquisitions, and market positioning. Additionally, by comparing capabilities across companies and industries, organizations can identify their competitive advantages, industry trends, potential disruptions, and effective resource allocations, leading to improved strategies and better market opportunities. Traditional methods for extracting business capabilities primarily rely on keyword matching or rule-based techniques, which often fail to capture the complex relationships between words in a sentence. This paper presents a comprehensive study on extracting business capabilities using dependency parsing techniques in natural language processing (NLP) to overcome limitations of the traditional methods and improve the accuracy of business capability extraction. Dependency parsing utilizes pre-trained language models to analyze the grammatical structure of sentences, identifying the dependencies between words and capturing their relationships. The feasibility and effectiveness of the proposed approach is demonstrated through experiments conducted on more than 40K real-world company descriptions crawled from Wikipedia.

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Adaptive Deep Learning Model to Enhance Smart Greenhouse Agriculture

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computers, Materials & Continua 2023年第11期77卷 2545-2564页

作者： Medhat A.Tawfeek Nacim Yanes Leila Jamel Ghadah Aldehim Mahmood A.Mahmood Department of Computer Science College of Computer and Information SciencesJouf UniversityP.O.Box 72314SkakaSaudi Arabia Department of Computer Science Faculty of Computers and InformationMenoufia UniversityP.O.Box 6121890MenoufiaEgypt Department of Software Engineering College of Computer and Information SciencesJouf UniversityP.O.Box 72314SkakaSaudi Arabia RIADI Laboratory La Manouba UniversityP.O.Box2010ManoubaTunisia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems and Technology Faculty of Graduate Studies and ResearchCairo UniversityP.O.Box 3753450GizaEgypt

The trend towards smart greenhouses stems from various factors,including a lack of agricultural land area owing to population concentration and housing construction on agricultural land,as well as water *** study proposes building a full farming adaptation model that depends on current sensor readings and available datasets from different agricultural research *** proposed model uses a one-dimensional convolutional neural network(CNN)deep learning model to control the growth of strategic crops,including cucumber,pepper,tomato,and *** proposed model uses the Internet of Things(IoT)to collect data on agricultural operations and then uses this data to control and monitor these operations in real *** helps to ensure that crops are getting the right amount of fertilizer,water,light,and temperature,which can lead to improved yields and a reduced risk of crop *** dataset is based on data collected from expert farmers,the photovoltaic construction process,agricultural engineers,and research *** experimental results showed that the precision,recall,F1-measures,and accuracy of the one-dimensional CNN for the tested dataset were approximately 97.3%,98.2%,97.25%,and 97.56%,*** new smart greenhouse automation system was also evaluated on four crops with a high turnover *** system has been found to be highly effective in terms of crop productivity,temperature management and water conservation.

关键词： Greenhouse wireless sensor network deep learning Internet of Things strategic crops monitoring smart irrigation

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Leaf Guard: Detecting the Diseases in Banana Leaf 5

Leaf Guard: Detecting the Diseases in Banana Leaf

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Yathurshan, K. Barathy, S. Karini, G. Sajitha, P. Abeygunawardhna, Pradeep Rajapakse, Kaushalya Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Engineering Electrical & Electronic Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

This research paper introduces 'Leaf Guard,' a mobile app designed for disease detection and management in banana trees, specifically tailored for Sri Lanka's agricultural landscape. Through the amalgamation of image processing, deep learning, and customized chemical recommendations, this app proficiently identifies early-stage diseases, offering tailored remedies. Catering to farmers and agricultural experts, 'Leaf Guard' provides an intuitive tool, revolutionizing disease management and bolstering sustainable banana cultivation practices. By seamlessly integrating these cutting-edge technologies, this research significantly advances disease detection methods, paving the way for environmentally friendly and economical interventions. The study's findings not only enrich the realm of disease detection technology but also hold promise for future innovations, fostering sustainable approaches to enhance banana cultivation. © 2023 IEEE.

关键词： Cultivation

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Brain Tumor Segmentation in Multimodal MRI Using U-Net Layered Structure

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computers, Materials & Continua 2023年第3期74卷 5267-5281页

作者： Muhammad Javaid Iqbal Muhammad Waseem Iqbal Muhammad Anwar Muhammad Murad Khan Abd Jabar Nazimi Mohammad Nazir Ahmad Department of Information Technology The Superior UniversityLahorePakistan Department of Software Engineering The Superior UniversityLahorePakistan Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahorePakistan Department of Computer Science Government College UniversityFaisalabadPakistan Deptment of Oral and Maxillofacial Surgery Faculty of DentistryUniversiti Kebangsaan MalaysiaKuala LumpurMalaysia Institute of IR4.0 Universiti Kebangsaan MalaysiaBangiSelangorMalaysia

The brain tumour is the mass where some tissues become old or damaged,but they do not die or not leave their *** brain tumour masses occur due to malignant *** tissues must die so that new tissues are allowed to be born and take their *** segmentation is a complex and time-taking problem due to the tumour’s size,shape,and appearance *** finding such masses in the brain by analyzing Magnetic Resonance Images(MRI)is a crucial task for experts and *** could not work for large volume images simultaneously,and many errors occurred due to overwhelming image *** main objective of this research study is the segmentation of tumors in brain MRI images with the help of digital image processing and deep learning *** research study proposed an automatic model for tumor segmentation in MRI *** proposed model has a few significant steps,which first apply the pre-processing method for the whole dataset to convert Neuroimaging Informatics Technology Initiative(NIFTI)volumes into the 3D NumPy *** the second step,the proposed model adopts U-Net deep learning segmentation algorithm with an improved layered structure and sets the updated *** the third step,the proposed model uses state-of-the-art Medical Image Computing and computer-Assisted Intervention(MICCAI)BRATS 2018 dataset withMRI modalities such as T1,T1Gd,T2,and Fluidattenuated inversion recovery(FLAIR).Tumour types in MRI images are classified according to the tumour *** of these masses carried by state-of-the-art approaches such that the first is enhancing tumour(label 4),edema(label 2),necrotic and non-enhancing tumour core(label 1),and the remaining region is label 0 such that edema(whole tumour),necrosis and *** proposed model is evaluated and gets the Dice Coefficient(DSC)value for High-grade glioma(HGG)volumes for their test set-a,test set-b,and test set-c 0.9795, 0.9855 and 0.9793, respectively.

关键词： Brain tumour segmentation magnetic resonance images modalities dice coefficient low-grade glioma U-Net

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${\sf Img2Acoustic}$Img2Acoustic: A Cross-Modal Gesture Recognition Method Based on Few-Shot Learning

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IEEE Transactions on Mobile Computing 2024年第3期24卷 1496-1512页

作者： Yongpan Zou Jianhao Weng Wenting Kuang Yang Jiao Victor C. M. Leung Kaishun Wu College of Computer Science and Software Engineering Shenzhen University Shenzhen China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen China Information Hub Hong Kong University of Science and Technology Guangzhou China

Acoustic-based human gesture recognition (HGR) offers diverse applications due to the ubiquity of sensors and touch-free interaction. However, existing machine learning approaches require substantial training data, making the process time-consuming, costly, and labor-intensive. Recent studies have explored cross-modal methods to reduce the need for large training datasets in behavior recognition, but they typically rely on open-source datasets that closely align with the target domain, limiting flexibility and complicating data collection. In this paper, we propose ${\sf Img2Acoustic}$ , a novel cross-modal acoustic-based HGR approach that leverages models trained on open-source image datasets (i.e., EMNIST, Omniglot) to effectively recognize custom gestures detected via acoustic signals. Our model incorporates a task-aware attention layer (TAAL) and a task-aware local matching layer (TALML), enabling seamless transfer of knowledge from image datasets to acoustic gesture recognition. We implement ${\sf Img2Acoustic}$ on commercial devices and conduct comprehensive evaluations, demonstrating that our method not only delivers superior accuracy and robustness compared to existing approaches but also eliminates the need for extensive training data collection.

关键词： Training Gesture recognition Acoustics Data models Sensors Feature extraction Target recognition Mobile computing Image recognition Few shot learning

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