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1st International Conference on Software, Systems and Information technology, SSITCON 2024

作者： Buvaneswari, Pr. Bhasgi, Shivani S Alabdeli, Haider Soundarya, M. Saravanan, T. Vit Bhopal University School of Electrical and Electronics Engineering Sehore India Maisammaguda School of Computer Science and Engineering Hyderabad India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai India New Prince Shri Bhavani College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350352931

The Rheumatoid Arthritis (RA) is a very common autoimmune disease that causes significant morbidity and mortality, therefore early diagnosis treatment is important. The RA is an autoimmune illness that affects the musculoskeletal system causing systemic, inflammatory and chronic effects, it becomes decreases physical function causing articular damage and fatigue. This paper proposed Addax Optimization Algorithm (AOA) with Convolutional Neural Network (CNN) for classification, which is efficiently achieve better accuracy. Initially data obtained from the RA dataset and pre-processing involved the min-max normalization is uniformly the feature vector. The combination of AOA is designed to optimize hyper parameters efficiently which is significantly reduce the high dimensionality and better accuracy in classifying of RA. The classification utilized CNN technique capturing complex patterns in image data effective for analysis the RA image and adaptive approach helps to find the best solution by using optimization approach. The performance of proposed method is evaluated with various metrics such as accuracy, precision, recall and f 1-score metrics on RA dataset. © 2024 IEEE.

关键词： Convolutional neural networks

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Traffic Priority-Aware medical Data Dissemination Scheme for IoT Based WBASN Healthcare Applications

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computers, Materials & Continua 2022年第6期71卷 4443-4456页

作者： Muhammad Anwar Farhan Masud Rizwan Aslam Butt Sevia Mahdaliza Idrus Mohammad Nazir Ahmad Mohd Yazid Bajuri Department of Computer Science NFC Institute of Engineering and Fertilizer ResearchFaisalabadPakistan Department of Statistics and Computer Science Faculty of Life Sciences Business ManagementUniversity of Veterinary and Animal SciencesLahorePakistan Department of Electronics Engineering NED University of Engineering and TechnologyKarachiPakistan School of Electrical Engineering Universiti Teknologi MalaysiaJohor BahruMalaysia Institute of IR4.0 Universiti Kebangsaan MalaysiaBangiSelangorMalaysia Department of Orthopedics and Traumatology Faculty of MedicineUniversiti Kebangsaan MalaysiaKuala LumpurMalaysia

Wireless Body Area Sensor Network(WBASN)is an automated system for remote health monitoring of *** under umbrella of Internet of Things(IoT)is comprised of small Biomedical Sensor Nodes(BSNs)that can communicate with each other without human *** BSNs can be placed on human body or inside the skin of the patients to regularly monitor their vital *** BSNs generate critical data as it is related to patient’s *** data traffic can be classified as Sensitive Data(SD)and Non-sensitive Data(ND)packets based on the value of vital *** data packets have different priority to *** ND packets may tolerate some delay or packet loss whereas,the SD packets required to be delivered on time with minimized packet loss otherwise it can be life threating to the *** this research,we propose a Traffic Priority-aware medical Data Dissemination(TPMD2)scheme forWBASN to deliver the data packets according to their priority based on the sensitivity of the *** assessment of the proposed scheme is carried out in various *** simulation results of the TPMD2 scheme indicate a significant improvement in packets delivery,transmission delay and energy efficiency in comparison with the existing schemes.

关键词： WBANS wearable technology priority routing eHealth applications remote health monitoring

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Accuracy Prediction using Machine Learning Techniques for Indian Patient Liver Disease

Accuracy Prediction using Machine Learning Techniques for In...

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Computational Intelligence and Sustainable engineering Solutions (CISES), International Conference on

作者： Shylaja DS Mekala Karunya Oliviya J Suriya Prakash J Computer Science Engineering Faculty of Engineering and Technology Jain University Bengaluru India

One of the principal cause of mortality in the countryis liver disease. Each year, liver disease kills more than 2.4 percent of all Indians. As a result,for the early intervention of liver disease, anautomated programme with higher accuracy and reliability is necessary. Specific machine learning models such as SVM, Logistic Regression, KNN, and ANN are being created for this purpose in order to foretell liver disease with more precision, accuracy, and reliability.

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Enhanced Body Composition Estimation from 3D Body Scans

Enhanced Body Composition Estimation from 3D Body Scans

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18th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2025

作者： Feng, Boyuan Zheng, Yijiang Cheng, Ruting Feng, Shuya Vaziri, Khashayar Hahn, James Department of Computer Science George Washington University WashingtonDC United States Department of Computer Science and Engineering University of Connecticut StorrsCT United States Department of Surgery The George Washington University Medical Faculty Associates WashingtonDC United States

Accurate body composition assessment is essential for evaluating health and diagnosing conditions like sar copenia and cardiovascular disease. Approaches for accurately measuring body composition, such as Dual Energy X-ray Absorptiometry (DXA) and Magnetic Resonance Imaging (MRI), are precise but costly and limited in accessibility. Some studies have explored predicting body composition by using shapes since 3D scanning techniques allow for precise and efficient digital measurements of body shape. This study introduces an enhanced method using 3D body scanning integrated with a part-to-global Multilayer Perceptron (MLP) network that incorporates predefined high-level features for body composition prediction. For lean mass es timation, our method achieved a root mean square error (RMSE) of 2.85 kg. For fat mass estimation, the RMSE was 2.50 kg, and for bone mineral content (BMC), the RMSE was 193.50 g. These results represent substantial improvements over existing methods, highlighting the effectiveness and reliability of our approach in accurately predicting body composition metrics. © 2025 by SCITEPRESS– science and technology Publications, Lda.

关键词： Diagnosis

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Real-Time Hydraulic Fluid Leak Detection in Heavy Machinery Using Cloud-Integrated Wireless Sensor Networks for Proactive Maintenance 10

Real-Time Hydraulic Fluid Leak Detection in Heavy Machinery ...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Kumaresh, P.S. Sivanantham, E. Shoba, L.K. Priya, W. Deva Mohankumar, N. Elango, B. K.L.N.College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Sivagangai India Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chenagalpattu Chennai Tamil Nadu Kattankulathur India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Symbiosis Institute of Technology Maharashtra Nagpur India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

This research introduces unique techniques for improving preventative servicing of heavy equipment using a real-time leak-detecting system for hydraulic fluid. The technology employs a network of sensors across the gear to monitor the hydraulics in real time. These wireless sensors communicate data to a cloud-based platform, where cloud computing analytics are used for real-time analysis. The cloud-integrated sensor network quickly detects hydraulic fluid leakage to address the critical demand for quick maintenance interventions. Preventing possible breakdowns and avoiding downtime are two areas where real-time skills shine. The cloud-based design enables remote monitoring and administration, which is essential for a preventative maintenance approach. Multiple pieces of heavy equipment were used in virtual and real-world tests to evaluate the proposed solution. The results reveal that the system is useful for real-time detection of hydraulic fluid leaks, which can potentially increase heavy equipment's overall dependability and performance. The system is a viable option for a wide range of industrial uses due to the incorporation of cloud computing, which guarantees scalability, flexibility, and accessibility. © 2024 IEEE.

关键词： Wireless sensor networks

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Versatile Tasks on Integrated Aerial Platforms Using Only Onboard Sensors: Control, Estimation, and Validation

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IEEE Transactions on Robotics 2025年 41卷 3518-3538页

作者： Wang, Kaidi Lai, Ganghua Yu, Yushu Du, Jianrui Sun, Jiali Xu, Bin Franchi, Antonio Sun, Fuchun Beijing Institute of Technology School of Mechatronical Engineering Beijing100081 China Beijing Institute of Technology School of Mechanical Engineering Beijing100081 China University of Twente Robotics and Mechatronics Lab Faculty of Electrical Engineering Mathematics and Computer Science Enschede7500 AE Netherlands Tsinghua University Department of Computer Science and Technology China

Connecting multiple aerial vehicles to a rigid central platform through passive spherical joints holds the potential to construct a fully-actuated aerial platform. The integration of multiple vehicles enhances efficiency in tasks like mapping and object reconnaissance. This paper proposes a control and state estimation framework for the Integrated Aerial Platform (IAP), enabling it to perform versatile tasks like object reconnaissance and physical interactive tasks with only onboard sensors. In the framework, the 6D motion control serves as the low-level controller, while the high-level controller comprises a 6D admittance filter and a perception-aware attitude correction module. The 6D admittance filter, serving as the interaction controller, is adaptable for aerial interaction tasks. The perception-aware attitude correction algorithm is carefully designed by adopting a geometric Model Predictive Controller (MPC). This algorithm, incorporating both offline and online calculations, proves to be well-suited for the intricate dynamics of an IAP. A 6D direct wrench controller is also developed for the IAP. Notably, both the interaction controller and the direct wrench controller operate without reliance on force/torque sensors. Instead, a wrench observer algorithm is devised, considering external disturbances. Additionally, based on the kinematics constraints of the multiple aerials in the platform, a fusion algorithm for multiple Visual-Inertial Odometry (VIO) and kinematics constraints is developed, providing more accurate localization. A prototype of the IAP is constructed, and its capabilities are demonstrated through experiments including perception-aware object reconnaissance, aerial mapping, aerial peg-in-hole task, and 6D contact wrench generation. All experiments are conducted exclusively with onboard sensors. These tasks exemplify the merits of the proposed IAP and validate the effectiveness of the proposed control framework and fusion algorithm. © 2004-2012

关键词： State estimation

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Defer No Time, Delays have Dangerous Ends: Slow HTTP/2 DoS Attacks into the Wild 14

Defer No Time, Delays have Dangerous Ends: Slow HTTP/2 DoS A...

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14th International Conference on COMmunication Systems and NETworkS, COMSNETS 2022

作者： Tripathi, Nikhil Shaji, Abhijith Kalayil Indian Institute of Information Technology Computer Science and Engineering Group Chittoor India Otto von Guericke University Faculty of Computer Science Magdeburg Germany

ISBN: (纸本)9781665421041

Slow Rate DoS attacks intend to prevent the legitimate clients from accessing the target application (web, mail, etc.) running over the victim server. These attacks target application layer protocols, and HTTP/1.1 is one of the most studied protocols against Slow Rate DoS attacks. HTTP/2, the successor of HTTP/1.1, is also found to be vulnerable to these attacks. However, the impact of these attacks on real HTTP/2 servers on the Internet has not been studied yet. In this preliminary work, we test the behaviour of Alexa top 500K websites on the Internet against Slow Rate DoS attacks. We first conduct experiments to find the websites that serve web content over HTTP/2. Subsequently, we test their web servers against the Slow Rate DoS attacks and furnish the results. We observe that several HTTP/2 servers on the Internet are vulnerable to these attacks, thereby, advocate developing a robust real-time detection strategy. Towards the end of the paper, we also discuss some possible defense mechanisms to counter the attacks. © 2022 IEEE.

关键词： HTTP

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Diverse Code Query Learning for Speech-Driven Facial Animation

arXiv

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arXiv 2024年

作者： Gu, Chunzhi Kuriyama, Shigeru Hotta, Katsuya The Department of Computer Science and Engineering Toyohashi University of Technology Toyohashi Japan The Faculty of Science and Engineering Iwate University Morioka Japan

Speech-driven facial animation aims to synthesize lip-synchronized 3D talking faces following the given speech signal. Prior methods to this task mostly focus on pursuing realism with deterministic systems, yet characterizing the potentially stochastic nature of facial motions has been to date rarely studied. While generative modeling approaches can easily handle the one-to-many mapping by repeatedly drawing samples, ensuring a diverse mode coverage of plausible facial motions on small-scale datasets remains challenging and less explored. In this paper, we propose predicting multiple samples conditioned on the same audio signal and then explicitly encouraging sample diversity to address diverse facial animation synthesis. Our core insight is to guide our model to explore the expressive facial latent space with a diversity-promoting loss such that the desired latent codes for diversification can be ideally identified. To this end, building upon the rich facial prior learned with vector-quantized variational auto-encoding mechanism, our model temporally queries multiple stochastic codes which can be flexibly decoded into a diverse yet plausible set of speech-faithful facial motions. To further allow for control over different facial parts during generation, the proposed model is designed to predict different facial portions of interest in a sequential manner, and compose them to eventually form full-face motions. Our paradigm realizes both diverse and controllable facial animation synthesis in a unified formulation. We experimentally demonstrate that our method yields state-of-the-art performance both quantitatively and qualitatively, especially regarding sample diversity. © 2024, CC BY-NC-ND.

关键词： Stochastic systems

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CoarseUCB: A Context-Aware Bitrate Adaptation Algorithm for VBR-encoded Video Streaming

CoarseUCB: A Context-Aware Bitrate Adaptation Algorithm for ...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Chengrun Yang Gangqiang Zhou Miao Hu Qinglin Zhao Di Wu School of Computer Science and Engineering Sun Yat-sen University Guangzhou Guangdong China School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology Macau China

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

Variable bitrate (VBR) encoding has gained considerable interest due to its capacity to enhance video quality and mitigate transmission congestion in contrast to constant bitrate (CBR) encoding. However, adaptive bitrate (ABR) streaming faces challenges when dealing with VBR-encoded videos, primarily stemming from the significant variability in chunk size and the consequent bitrate fluctuations. This paper proposes CoarseUCB, a context-aware online learning algorithm for bitrate adaptation in VBR-encoded videos. CoarseUCB considers important aspects of VBR-encoded video streaming and uses the upper confidence bound (UCB) method for bitrate selection. The UCB method does not require precise bandwidth estimation and balances the exploration and exploitation of each action effectively. Additionally, CoarseUCB accounts for the impact of multiple future video chunks when making the bitrate decision for the current chunk. To evaluate the effectiveness of CoarseUCB, we conduct experiments to assess its efficiency. The results show that CoarseUCB delivers a higher average user quality of experience (QoE) compared to state-of-the-art ABR algorithms, resulting in an improvement of up to 9.81%.

关键词： Wireless communication Fluctuations Bit rate Streaming media Throughput Encoding Quality assessment

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User Mobility Prediction in 5G Networks Using Recurrent Neural Networks

User Mobility Prediction in 5G Networks Using Recurrent Neur...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Shrestha, Anubhav Sharma, Vikalp Hussein, Layth Aishwarya, M. Satyanarayana, A.N. Saimanohar, T. Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Jhanjeri Mohali140307 India Ies College of Technology Department of Computer Science & Engineering M.P. Bhopal India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Sreenidhi Institute of Science and Technology Ece Department Hyderabad India Saveetha Institute of Medical and Technical Sciences Department CSE-AI Tamilnadu Chennai India

ISBN: (纸本)9798350390759

The use of new possibilities introduced by 5G networks also creates new problems and concerns, specifically in the field of user mobility in wireless communication systems. In this paper, the Authors investigate the effectiveness of Recurrent Neural Networks (RNNs) in the mobility prediction context in order to exploit the temporal component in the users movement data for improved prediction performance. To drive this process, the input datasets are obtained from the GeoLife GPS Trajectories dataset, then follow the data pre-processing steps, constructing an LSTM-based RNN model, and using Python with TensorFlow and Keras. From the analyses, we detected that the accomplishment of using LSTM and hybrid RNN-CNN is significant and higher than the traditional approaches of EN, RFM, and CL. The test results demonstrate that the LSTM model can reduce the MAE and RMSE values by 19. 49% and 14. 82%, respectively. However, the execution of these models has some issues that include compute time, instantaneous prediction that is needed in many real-world applications, and case. Such problems can be solved with the help of such approaches as edge computing and distributed processing. Besides, it is pivotal to put measures in place to maintain the confidentiality and security of user information. This paper highlights the applicative potential of RNNs in redesigning user mobility prediction in 5G networks, and thus helping toward a better ownership of the network players and assets. © 2024 IEEE.

关键词： 5G mobile communication systems

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