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Emerging Trends in Engineering and Medical Sciences (ICETEMS), International Conference on

作者： G. ramkumar M Swarna Madhini M P Kalpana devi Pallavi Giri Ala'a Al Sherideh Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai Tamil Nadu India Department of ECE Dr. MGR Educational and Research Institute Chennai Tamil Nadu India Department of ECE S.A. Engineering College Chennai Tamil Nadu India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Civil Engineering Laxminarayan Innovation Technological University Nagpur India Department of Cyber Security College of Information Technology Zarqa University zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331529352

ISBN: (纸本)9798331529390

The prediction of fetal health conditions is a critical aspect of prenatal care, and accurate detection of potential complications can greatly improve clinical outcomes. This paper proposes a novel Gradient distance Vector Model (GdVM) integrated with a hybrid deep learning architecture for fetal health prediction. The model combines dimensionality reduction using a Hybrid AutoEncoder, temporal sequence modeling via Long Short-Term Memory (LSTM) and Gated recurrent Units (GrU), and classification with Extreme Gradient Boosting (XGBoost). The proposed GdVM model was evaluated on publicly available fetal health datasets and achieved an impressive accuracy of 97.81%, outperforming nine existing models, including random Forest, Support Vector Machine, and LSTM + GrU. In addition to superior accuracy, the GdVM model demonstrated excellent performance across other metrics, such as precision, recall, and F1-score. The model's ability to handle high-dimensional data and capture temporal patterns in fetal health metrics makes it a robust tool for predicting normal, suspected, and pathological fetal conditions. This approach offers significant potential for improving prenatal monitoring and early diagnosis of fetal complications.

关键词： Measurement deep learning Support vector machines Pathology Accuracy Predictive models Vectors Long short term memory random forests Monitoring

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Energy Proficient and dependable Cluster routing in Wireless Sensor Network

Energy Proficient and Dependable Cluster Routing in Wireless...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： S. Hemavathi Jeya Ganesan J G Geetha Kishore Kumar K r. Kohila Department of Computer Science Engineering Sri Sairam Engineering College Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Sri Sairam Institute of Technology Chennai Tamil Nadu India Department of Computer Science Engineering B V Raju Institute of Technology Hyderabad Telangana India Department of Computer Science Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science Engineering (Cyber Security) Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India

Huge energy utilization information of structures is created with the improving information technology. The energy utilization information is processed by an energy depletion observing system through the wireless sensor network (WSN). Conventional security approaches can face external attacks. Nevertheless, they cannot proficiently solve interior attacks triggered by the captured nodes. Energy-efficient and dependable Cluster routing (EPdC) in WSN. This approach aims to reduce energy expenditure and enhance reliable routing in WSNs. The highest energy and highest available bandwidth are selected as a cluster head (CH). In every cluster, the selected CH has received the observed information from the cluster member and then forwarded it to the BS through a relay node. The forwarder relay node is selected based on the average energy faith and transmission faith during data transmission. The whole faith is greater than the average value that node is selected as a data forwarder from cluster to BS in the WSN. Furthermore, the Path Optimization Algorithm (POA) is used to detect the obstacle and build the obstacle-aware route efficiently. The simulation results explain why the proposed method has better residual energy. In addition, it minimized both the network delay and the packet loss rate in the WSN.

关键词： Wireless sensor networks Simulation Packet loss Bandwidth routing Energy efficiency delays

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A Machine Learning Approach for the Underwater Investigation of Malicious Activity in a Wireless Sensor Network

A Machine Learning Approach for the Underwater Investigation...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Murali dhar M S Mahaveerakannan r F. ravindaran Kommisetti Murthyraju Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu Department of Spatial Informatics The Institute of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering (Cyber Security) Karpagam College of Engineering Coimbatore Tamil Nadu Department of Electronics and Communication Engineering Shri Vishnu Engineering College For Women BHIMAVARAM Andha Pradesh

Underwater Wireless Sensor Networks (UWSN) have drawn increased interest from researchers in recent years due to advancements in underwater tracking, the implementation of multiple uses, and marine monitoring. Both researchers and business are drawn to the UWSN field. It is also susceptible to hostile attacks and threats because of the harsh undersea surroundings, its own capabilities, and the open acoustic channel. These features make it simple for attackers to intercept information across the source and destination. Many studies have been done to secure UWSN from malicious attacks, but the majority of them rely on a threshold that is determined before deployment or a training set of data. Because there is no proven security foundation, it is difficult and challenging for UWSN to identify harmful attacks. To identify harmful attacks in a UWSN, we support vector machines in this research. SVM produces good results and requires significantly less time to train than neural networks. This study will help the researchers design the routing protocols to cover the known security threats and help industries manufacture the devices to observe these threats and security issues.

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Approach to cognitive foundation® for the Innovative Optical and Wireless Network (IOWN)

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NTT Technical review 2020年第6期18卷 11-16页

作者： Hasebe, Katsuyuki Aoki, daisuke Kusakabe, Yusuke Kanzaki, Makoto Kudo, Ichiro Ikebe, Takashi Technology Planning Department NTT Platform Strategy Section Technology Planning Department NTT R&D Vision Group Research and Development Planning Department NTT

There is an urgent demand for information and communication technology (ICT) to meet the needs of industrial and social systems. These systems include the management of rapidly increasing devices and diverse digital services. In this article, we introduce NTT Group's approach to fully automated, autonomous, and self-evolving life-cycle management of ICT resources, including the deployment and optimization of such resources, to provide applications and solutions. © 2020 Nippon Telegraph and Telephone Corp.. All rights reserved.

关键词： Wireless networks

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Federated Learning-Based Intelligent Indoor Smoke and Fire detection System for Smart Buildings

Federated Learning-Based Intelligent Indoor Smoke and Fire D...

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Telecommunications and Intelligent Systems (ICTIS), International Conference on

作者： Mohamed rafik Aymene Berkani Ammar Chouchane Yassine Himeur Anes Abdennebi Seref Sagiroglu Abbes Amira Research Laboratory in Advanced Electronics Systems (LSEA) University Yahia Fares of Medea Medea Algeria Laboratory of LI3C University Biskra University Center of Barika Barika Algeria College of Engineering and Information Technology University of Dubai Dubai United Arab Emirates Software and Information Technology Engineering École de Technologie Supérieure Montréal Canada Artificial Intelligence and Big Data Analytics Security R&D Center Gazi University Ankara Turkey Department of Computer Science University of Sharjah Sharjah United Arab Emirates

ISBN: (数字)9798331527396

ISBN: (纸本)9798331527402

Ensuring safety in smart buildings is crucial due to the increasing prevalence of smoke and fire hazards in modern environments. This paper introduces a novel privacy-preserving FL approach based on a CNN1d for smoke and fire detection in smart buildings. Our system integrates data from wearable environmental sensors to train a lightweight, edge-deployable dL-CNN1d model, ensuring data privacy while enabling collaborative learning across distributed clients using Federated Averaging (FedAvg) aggregation. Experiments conducted on a comprehensive air measurement dataset for smoke and fire detection demonstrate exceptional performance, with the global model achieving 99.97% accuracy, 99.96% precision, in smoke and fire recognition. Our model demonstrates a low communication cost of 0.4 MB, underscoring its efficiency for real-time applications. Our FL-based approach represents a significant step towards balancing the need for robust safety systems with growing privacy concerns in smart building environments.

关键词： Privacy Smart buildings Costs Federated learning Atmospheric modeling data models Convolutional neural networks Wearable sensors Monitoring Intelligent sensors

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deep Learning-Based Multimodal Feature Interaction-Guided Fusion: Enhancing the Evaluation of EGFr in Advanced Lung Adenocarcinoma

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Academic radiology 2025年

作者： Xu, Junhui Feng, Bao Chen, Xiangmeng Wu, Fei Liu, Yu Yu, Zhaole Lu, Senliang duan, Xiaobei Chen, Xiaojuan Li, Kunwei Zhang, Weibin dai, Xisheng School of Automation Guangxi University of Science and Technology Guangxi Liuzhou China Laboratory of Artificial Intelligence of Biomedicine Guilin University of Aerospace Technology Guangxi Guilin China Department of Radiology Jiangmen Central Hospital Guangdong Jiangmen China IFlytek Technology Co. Ltd.Artificial Intelligence Cloud Service Platform R&D Building No. 666 Wangjiang West Road High-Tech Zone Anhui Free Trade Pilot Zone Hefei China Nuclear medicine Jiangmen Central Hospital Guangdong Jiangmen China Department of Radiology The Fifth Affiliated Hospital of Sun Yat-sen University Guangdong Zhuhai China Department of Pathology Jiangmen Central Hospital Guangdong Jiangmen China

rationale and Objectives: The aim of this study is to develop a deep learning-based multimodal feature interaction-guided fusion (dL-MFIF) framework that integrates macroscopic information from computed tomography (CT) images with microscopic information from whole-slide images (WSIs) to predict the epidermal growth factor receptor (EGFr) mutations of primary lung adenocarcinoma in patients with advanced-stage disease. Materials and Methods: data from 396 patients with lung adenocarcinoma across two medical institutions were analyzed. The data from 243 cases were divided into a training set (n = 145) and an internal validation set (n = 98) in a 6:4 ratio, and data from an additional 153 cases from another medical institution were included as an external validation set. All cases included CT scan images and WSIs. To integrate multimodal information, we developed the dL-MFIF framework, which leverages deep learning techniques to capture the interactions between radiomic macrofeatures derived from CT images and microfeatures obtained from WSIs. results: Compared to other classification models, the dL-MFIF model achieved significantly higher area under the curve (AUC) values. Specifically, the model outperformed others on both the internal validation set (AUC = 0.856, accuracy = 0.750) and the external validation set (AUC = 0.817, accuracy = 0.708). decision curve analysis (dCA) demonstrated that the model provided superior net benefits(range 0.15–0.87). delong's test for external validation confirmed the statistical significance of the results (P<0.05). Conclusion: The dL-MFIF model demonstrated excellent performance in evaluating and distinguishing the EGFr in patients with advanced lung adenocarcinoma. This model effectively aids radiologists in accurately classifying EGFr mutations in patients with primary lung adenocarcinoma, thereby improving treatment outcomes for this population. © 2025 The Association of University radiologists

关键词： deep Learning EGFr Feature fusion Lung cancer Multimodal

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Metabolite Profiling of Herbal Medicines using an Artificial Intelligence Approach-A Concise review

Metabolite Profiling of Herbal Medicines using an Artificial...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Ajay S Shet Somasundaram r Sravan Kumar G S. Kaliappan S.r. ramprasad Maloth Srinivas Department of CSE Canara Engineering College Mangalore Karnataka India Management Studies Department Kongu Engineering College Erode TN India Dept. of CSE (Cyber Security) CVR College of Engineering Hyderabad Telangana India Division of Research and Development Lovely Professional University Jalandhar Punjab India Dept. of CSE School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai TN India Dept. of CSE (AIML) St. Martin's Engineering College Secunderabad Telangana India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Artificial intelligence (AI) is being developed extremely swiftly in medicine. A structured review should be conducted to comprehend the current status of AI models since they have been introduced in Traditional medicine to Investigate and classify the present applications of AI in TM. Following the technique suggested by the Joanna Briggs Institute, a thorough scoping review was conducted. To find papers on AI and CAM, three databases were searched: PubMed, Embase, and Cochrane Library. Studies published only in English since 2000 are taken into account. Those without mention of traditional medicine or AI methods were also disqualified, as were studies that were not peer-reviewed. All pertinent studies were located using a comprehensive search technique. This review demonstrated the potential use of AI models in TM; nevertheless, to develop AI in TM in the era of digital health, future research should concentrate on validating and verifying the models by carrying out a sizable clinical investigation.

关键词： reviews databases Machine learning Predictive models Prediction algorithms Knowledge discovery data models Libraries data mining Artificial intelligence

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Best practices for machine learning in antibody discovery and development

arXiv

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arXiv 2023年

作者： Wossnig, Leonard Furtmann, Norbert Buchanan, Andrew Kumar, Sandeep Greiff, Victor LabGenius Ltd. The Biscuit Factory 100 Drummond Road London LondonSE16 4DG United Kingdom Department of Computer Science University College London 66-72 Gower St LondonWC1E 6EA United Kingdom R&D Large Molecules Research Platform Sanofi Deutschland GmbH Industriepark Höchst Frankfurt am Main Germany Biologics Engineering R&D AstraZeneca CambridgeCB2 0AA United Kingdom Computational Protein Design and Modeling Group Computational Science Moderna Therapeutics 200 Technology Square CambridgeMA02139 United States Department of Immunology Oslo University Hospital University of Oslo Oslo Norway

Over the past 40 years, the discovery and development of therapeutic antibodies to treat disease has become common practice. However, as therapeutic antibody constructs are becoming more sophisticated (e.g., multi-specifics), conventional approaches to optimisation are increasingly inefficient. Machine learning (ML) promises to open up an in silico route to antibody discovery and help accelerate the development of drug products using a reduced number of experiments and hence cost. Over the past few years, we have observed rapid developments in the field of ML-guided antibody discovery and development (d&d). However, many of the results are difficult to compare or hard to assess for utility by other experts in the field due to the high diversity in the datasets and evaluation techniques and metrics that are across industry and academia. This limitation of the literature curtails the broad adoption of ML across the industry and slows down overall progress in the field, highlighting the need to develop standards and guidelines that may help improve the reproducibility of ML models across different research groups. To address these challenges, we set out in this perspective to critically review current practices, explain common pitfalls, and clearly define a set of method development and evaluation guidelines that can be applied to different types of ML-based techniques for therapeutic antibody d&d. Specifically, we address in an end-to-end analysis, challenges associated with all aspects of the ML process and recommend a set of best practices for each stage. © 2023, CC BY-NC-Nd.

关键词： Antibodies

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SynFacePAd 2023: Competition on Face Presentation Attack detection Based on Privacy-aware Synthetic Training data

SynFacePAD 2023: Competition on Face Presentation Attack Det...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Meiling Fang Marco Huber Julian Fierrez raghavendra ramachandra Naser damer Alhasan Alkhaddour Maksim Kasantcev Vasiliy Pryadchenko Ziyuan Yang Huijie Huangfu Yingyu Chen Yi Zhang Yuchen Pan Junjun Jiang Xianming Liu Xianyun Sun Caiyong Wang Xingyu Liu Zhaohua Chang Guangzhe Zhao Juan Tapia Lazaro Gonzalez-Soler Carlos Aravena daniel Schulz Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Spain Norwegian University of Science and Technology (NTNU) Norway ID R&D Inc New York US School of Cyber Science and Engineering Sichuan University Chengdu China School of Computer Science and Technology Harbin Institute of Technology Harbin China School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture China Biometrics and Security Research Group Hochschule Darmstadt Darmstadt Germany I+D Vision Center Santiago Chile

This paper presents a summary of the Competition on Face Presentation Attack detection Based on Privacy-aware Synthetic Training data (SynFacePAd 2023) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition attracted a total of 8 participating teams with valid submissions from academia and industry. The competition aimed to motivate and attract solutions that target detecting face presentation attacks while considering synthetic-based training data motivated by privacy, legal and ethical concerns associated with personal data. To achieve that, the training data used by the participants was limited to synthetic data provided by the organizers. The submitted solutions presented innovations and novel approaches that led to outperforming the considered baseline in the investigated benchmarks.

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SynFacePAd 2023: Competition on Face Presentation Attack detection Based on Privacy-aware Synthetic Training data

arXiv

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arXiv 2023年

作者： Fang, Meiling Huber, Marco Fierrez, Julian ramachandra, raghavendra damer, Naser Alkhaddour, Alhasan Kasantcev, Maksim Pryadchenko, Vasiliy Yang, Ziyuan Huangfu, Huijie Chen, Yingyu Zhang, Yi Pan, Yuchen Jiang, Junjun Liu, Xianming Sun, Xianyun Wang, Caiyong Liu, Xingyu Chang, Zhaohua Zhao, Guangzhe Tapia, Juan Gonzalez-Soler, Lazaro Aravena, Carlos Schulz, daniel Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Spain Norway ID R&D Inc. New York United States School of Cyber Science and Engineering Sichuan University Chengdu China School of Computer Science and Technology Harbin Institute of Technology Harbin China School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture China Biometrics and Security Research Group Hochschule Darmstadt Darmstadt Germany I+D Vision Center Santiago Chile

关键词： Competition

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