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Pervasive Computational Technologies (ICPCT), International Conference on

作者： D. Santhakumar Gopalakrishnan Soundararajan Suman Chintala E. Bharath Krishna Murthy Inumula Kuldeep Chouhan Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Thandalam – Chennai Tamil Nadu Muscat College Ruwi Sultanate of Oman Business Intelligence Architect USA Department of Artificial Intelligence & Data Science C K College of Engineering & Technology Anna University Cuddalore Tamil Nadu Symbiosis Institute of International Business (SIIB) Symbiosis International (Deemed University) Pune Maharashtra India Department of Computer Science and Applications Sharda School of Engineering and Technology Sharda University Greater Noida India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

In order to create enduring partnerships that are essential to attaining long-term economic success, supply chain management, or SCM, is essential. Strict standards and decision-making procedures are necessary for efficient SCM, and they have a big influence on the final results. In order to efficiently manage inventory levels and logistical procedures, this study offers a complex ML system that combines demand forecasts and optimisation methods. For predicting demand, the approach uses a Generative Adversarial networks (GAN) model, taking advantage of its capacity to process time-series data and identify intricate trends. The algorithm achieves an impressive 96.4% forecasting of demand accuracy by examining extensive historical sales data and a variety of supply chain variables. In order to optimise inventory rules, the investigation also uses optimisation techniques, particularly Genetic Algorithms (GA) in conjunction with Simulated Annealing (SA). The model demonstrated significant improvements when implemented to an actual commercial logistics system. Notably, it reduced storage expenses by 19% and increased order fulfillment rates by 24%, proving its capacity for combining service standards with financial efficiency. The findings highlight the significant impact of ML on improving supply chain efficiency and show how it can result in significant cost savings and improve service quality.

关键词： Supply chain management Costs Accuracy Supply chains Demand forecasting Machine learning Predictive models Generative adversarial networks Standards Genetic algorithms

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Deep Neural Quality of Service Prediction for Unmanned Aircraft System Communications

Deep Neural Quality of Service Prediction for Unmanned Aircr...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Patrick Purucker Christian Reil Alfred Höß Björn W. Schuller Department of Electrical Engineering Media Technology and Computer Sciences University of Applied Sciences Amberg-Weiden Amberg Germany CHI MRI Technical University of Munich Germany Munich Data-Science Institute (MDSI) Germany Munich Center for Machine Learning (MCML) Germany GLAM Imperial College London UK

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

Commercial Unmanned Aircraft Systems (UAS) have a wide range of applications, including package delivery, inspection and search and rescue missions. For the operation of Unmanned Aircraft Vehicles (UAV) Beyond Visual Line of Sight (BVLOS), reliable long-range communication is essential. The cellular network is one possible solution, but there are issues such as signal loss and frequent handovers at higher altitudes. To mitigate these issues, our work proposes the use of two cellular links from different providers prioritised according to Quality of Service (QoS) prediction. We evaluate multiple AI-based model architectures for the prediction, and find that the model consisting of Gated Recurrent Units (GRU) and convolutional layers outperforms the others. The models are trained and tested on real-world data and show a reduction in latency peaks, thereby increasing connection resilience. Moreover, the prediction pipeline is designed to be executable on the UAV side and is not limited to a specific geographical area, making it applicable to real-world scenarios. Finally, we present a pre-flight path planning algorithm that takes QoS into account when calculating the flight path in order to further improve communication. To support the research community, we publicly share the dataset used to obtain our results.

关键词： Adaptation models Heuristic algorithms Quality of service computer architecture Predictive models Autonomous aerial vehicles Modems

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A Gated Recurrent Unit Based Continual Normalization Model for Arrythmia Classification Using ECG Signals

A Gated Recurrent Unit Based Continual Normalization Model f...

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Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE), International Conference on

作者： Chinthakunta Manjunath Hemalatha K.L Vooradi Sandya G Sunil Aboothar Mahmood Shakir Department of CSE CHRIST(Deemed to be University) Bengaluru India Department of Information Science and Engineering Sri Krishna Institute of Technology Bengaluru India Department of CSE(Data Science) CMR Technical Campus Hyderabad India School of Computer Science & Artificial Intelligence SR University Warangal India The Islamic University Najaf Iraq

In this world, around 31% of the deaths are commonly caused because of cardiovascular diseases. Around 80% of sudden deaths occur due to cardiac arrhythmias and heart diseases. The mortality rate has increased for cardiac disease and therefore early heart disease detection is significant to preclude patients from dying. At the initial phase, the heart disease is detected by analyzing abnormal heartbeats. The existing models failed to select the features before performing the extraction of features. The developed model examined MIT-BIB database to surpass the overfitting issue. Therefore, in the present research work, the Gated Recurrent Unit (GRU) based Continual Normalization (CN) classifier is used to speed up the training to a higher learning rate to enable simpler learning for the standard deviation of the neurons' output. The extracted features were used to classify Electrocardiogram (ECG) signals into 5 important classes named as N, S, V, F & Q which denote the kinds of arrhythmia. The findings revealed that the proposed GRU based Continual Normalization technique obtained an accuracy of 99.41% which is better when compared with the existing researches.

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EMO: An Ontology for Supporting the Management of Epidemic

EMO: An Ontology for Supporting the Management of Epidemic

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Applied Physics and Computing (ICAPC), International Conference on

作者： Yongchao Gao Qiuyue Wang Mark S. Fox Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Qilu University of Technology (Shandong Academy of Sciences) Jinan China Standard Institute of Emerging Technologies and Innovations Jinan China Urban Data Centre University of Toronto Toronto Canada

In order to improve the coverage of concepts in epidemic management, this paper constructs an epidemic management ontology EMO to help manage and analyse epidemic data. First, based on the ISO/lEC 5087 city data model series of standards and TOVE method, EMO is constructed using Protégé. Secondly, the data types and their relationships required in the epidemic management use cases are defined. Finally, the instance data help to verify the ability of EMO to represent the city epidemic management data. Results show that EMO provides a clear, accurate and unified definition of epidemic management data, improves the efficiency of data sharing and integration, and reduces the difficulty of developing epidemic management tools.

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Enhancing Energy Efficiency in WSN Through Multiple Cluster Head Selection and Fuzzy Logic

Enhancing Energy Efficiency in WSN Through Multiple Cluster ...

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Advances in Modern Age Technologies for Health and Engineering science (AMATHE), International Conference on

作者： S. Jagadeesh P Suresh Pandiarajan Jayaraman Kumarappan Karthika P.N. Karthikayan Rajesh Devaraj Department of Computer Science Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai ECE P. S R R. College Of Engineering Sivakasi Birla Institute of Technology and Science Pilani- Dubai Campus - Academic City - Dubai Department of IT Panimalar Engineering College Chennai India Department of Information technology St Joseph's institute of technology CHENNAI Controlled Networks Solution Bangalore

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

Recent developments in microelectronics have made possible the widespread use of wireless sensor networking (WSN). Timely and dependable delivery of event data is crucial in WSN applications, allowing for prompt reaction and corrective measures to be implemented. However, there is a communication lag among the sensor terminal and the base station due to the restricted power supply. The time- and energy-saving benefits of WSNs can be further enhanced by constructing a routing algorithm using clustering techniques. However, since the terminals have different power needs, the WSN is the most efficient and effective routing option. In this work, we present a novel routing protocol for WSNs based on the principles of energy efficiency and fuzzy logic: the Fuzzy Based Energy Efficient Multiple Cluster Head Selection Routing Protocol. Clustering nodes and choosing a Cluster Head (CH) from each cluster to relay data to the Cluster Head Leader (CHL) are both steps in the routing process. After then, data is compiled by the cluster leaders and sent to the BS. Fuzzy logic is used for selecting cluster heads and cluster head leaders, and the shortest energy channel is used for data transmission. The Bacterial Foraging Optimization (BFO) is a method used to identify and select the most optimal route between a source node, referred to as CH, and a destination node, referred to as BS (Base Station). This algorithm aims to find the route that minimizes certain criteria, such as distance, energy consumption, or latency. By utilizing various optimization techniques, BFO efficiently determines the best path for data transmission between CH and BS in a network. The simulation results demonstrated that the proposed methodology exhibited superior energy consumption performance.

关键词： Fuzzy logic Wireless sensor networks Energy consumption Power supplies Clustering algorithms Routing Energy efficiency

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Scheduling of wireless edge networks for feedback-based interactive applications

arXiv

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arXiv 2021年

作者： Zoppi, Samuele Champati, Jaya Prakash Gross, James Kellerer, Wolfgang Department of Communication Networks Technical University of Munich Germany IMDEA Networks Institute Spain School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden

Interactive applications with automated feedback will largely influence the design of future networked infrastructures. In such applications, status information about an environment of interest is captured and forwarded to a compute node, which analyzes the information and generates a feedback message. Timely processing and forwarding must ensure the feedback information to be still applicable;thus, the quality-of-service parameter for such applications is the end-to-end latency over the entire loop. By modelling the communication of a feedback loop as a two-hop network, we address the problem of allocating network resources in order to minimize the delay violation probability (DVP), i.e. the probability of the end-to-end latency exceeding a target value. We investigate the influence of the network queue states along the network path on the performance of semi-static and dynamic scheduling policies. The former determine the schedule prior to the transmission of the packet, while the latter benefit from feedback on the queue states as time evolves and reallocate time slots depending on the queue's evolution. The performance of the proposed policies is evaluated for variations in several system parameters and comparison baselines. Results show that the proposed semi-static policy achieves close-to-optimal DVP and the dynamic policy outperforms the state-of-the-art algorithms. © 2021, CC BY.

关键词： Interactive devices

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Correction to: Advanced model based machine learning technique for early stage prediction of ankylosing spondylitis under timely analysis with featured textures

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Multimedia Tools and Applications 2024年第26期83卷 68415-68420页

作者： Ahammad, Shaik Hasane Jayaraj, R. Shibu, S. Sujatha, V. Prathima, Ch Leo, L. Megalan Prabu, R. Thandaiah Hossain, Md.Amzad Rashed, Ahmed Nabih Zaki Department of ECE Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of Data Science and Buisness Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India Department of Master of Computer Applications S.A. Engineering College Chennai India Department of Data Science School of Computing Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College) Tirupati India Deptartment of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS Saveetha University Chennai India Department of Electrical and Electronic Engineering Jashore University of Science and Technology Jashore Bangladesh Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf Egypt

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A Drug Box Recognition System Based on Deep Learning and Cloud-Edge Collaboration for Pharmacy Automation

A Drug Box Recognition System Based on Deep Learning and Clo...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Honglei Zhu Anming Dong Jiguo Yu Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Key Laboratory of Computing Power Network and Information Security Qilu University of Technology (Shandong Academy of Sciences) Jinan China School of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Big Data Institute Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

Drug box recognition is an integral part of the pharmacy automation system (PAS), which checks for discrepancies between the drugs provided by the system and the doctor’s prescription with the help of modern image recognition techniques. Although deep learning has facilitated the development of pharmacy automation systems, practical applications still face challenges. First, pharmacies update drugs frequently, and many pharmacies across the country are trained independently, which requires consuming massive computational resources. Secondly, the equipment performance of pharmacies is poor, and it’s hard to guarantee the real-time and accuracy of recognition. Therefore, we propose a cloud-side collaborative drug box recognition system. When new drugs are added, the new model is trained in the cloud and uniformly distributed to each edge through this architecture. In terms of real-time recognition, an improved lightweight YOLOv5 model is added to the edge part, and a text recognition module is deployed in the cloud to validate the recognition results at the edge. This cloud-edge collaboration architecture not only ensures the accuracy of recognition, but also reduces the burden on the compute edge, ensures the real-time *** results show that the proposed cloud-edge architecture reduces the recognition latency by 37.18% and achieves a precision of 97.47%. The improved YOLOv5 model reduces GFLOPs by 45% while achieving a 1.6% precision improvement.

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Optimizing Business Performance with Cloud Computing: A Scalable and Efficient Approach

Optimizing Business Performance with Cloud Computing: A Scal...

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Augmented Intelligence and Sustainable Systems (ICAISS), International Conference on

作者： Aravind Nuthalapati Arun M Prajitha C Rinesh S Betzy Babu Thoppil Abubeker K M Big Data and Cloud Architect Microsoft Charlotte Department of Mechanical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Thandalam India Department of ECE Centre for Interdisciplinary Research Karpagam Academy of Higher Education Coimbatore India Department of Computer Science and Engineering V.S.B College of Engineering Technical Campus Coimbatore India Department of Computer Science and Engineering Amal Jyothi College of Engineering (Autonomous) Kerala India Department of Electronics and Communication Engineering Amal Jyothi College of Engineering (Autonomous) Kerala India

ISBN: (数字)9798331507244

ISBN: (纸本)9798331507251

Cloud computing has emerged as a transformative force in modern business, offering unparalleled opportunities to enhance efficiency and scalability. This study investigates how unlocking the power of cloud computing can revolutionize business operations, enabling organizations to adapt to dynamic market demands. Despite their potential, existing methods often face challenges such as high implementation costs, security vulnerabilities, and limited integration with legacy systems, which hinder optimal utilization. This research proposes a novel Business Operations framework using Cloud Computing Technologies (BO-CCT) to address these issues. This framework integrates advanced cloud technologies with business processes to streamline operations, reduce costs, and improve scalability. The BO-CCT framework emphasizes secure data management, seamless integration, and real-time analytics to resolve existing limitations. The proposed method empowers businesses by leveraging cloud computing for enhanced decision-making, resource optimization, and operational agility. Findings indicate that the BO-CCT framework significantly improves business efficiency, reduces operational costs, and enables scalable growth. This study concludes that adopting cloud computing through the BO-CCT framework can revolutionize business operations, providing a competitive edge in today's digital economy.

关键词： Cloud computing Costs Scalability Decision making Companies Aerodynamics Real-time systems Security Optimization Business

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Graph Convolutional networks Based Standard Essential Patents Identification

Graph Convolutional Networks Based Standard Essential Patent...

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International Joint Conference on Neural networks (IJCNN)

作者： Weidong Liu Rui Li Wei Tang College of Computer Science Inner Mongolia University China Hohhot China Institute of Scientific and Technical Information of China China Hohhot China Inner Mongolia Key laboratory of Social Computing and Data Processing China Hohhot China Computer College of Inner Mongolia University Inner Mongolia University Huhhot China Computer College of Inner Mongolia University

The standard essential patents are some patents for which the patent technology solutions are adopted by the technological standard. The identification of standard essential patents is an important task in the standardization. With the sharply increasing number of patents and much more diversity of standard domains, the manual standard essential patents identification methods to check the patent by word for word, are not applicable for the big patent data processing. The automatic standard essential patents identification methods face some challenges from the extraction and representation of features to the construction and learning of models. To solve the above issues, we develop a graph convolutional network based standard essential patents identification. We propose a hypothesis that the standard essential patents under the same standard are structure-association and the associated relations can be used in standard essential patents identification. Herein, we extract the co-occurrence relations including keyword-keyword relation, document-keyword relation. We construct a heterogeneous graph consisting of patent nodes, keyword nodes and their co-occurrence relations, which include rich structural information for the identification of standard essential patents. Thereafter, a graph convolutional network is developed to identify the potential standard essential patents. Since the number of standard essential patents and nonstandard essential patents is imbalance in practice, we utilize the sampling method to alleviate the imbalanced issue on the graph. To validate the effectiveness of the proposed model, we conduct experiments on five datasets of patent in the real world and the experimental results show that the proposed model outperforms existing research in the identification of standard essential patents.

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