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science, engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Lateef Adesola Akinyemi Olamide Peter Oshinuga Sunday Oladayo Oladejo Stephen Obono Ekwe Mbuyu Sumbwanyanbe Ernest Mnkandla Oluwagbemiga Omotayo Shoewu Departments of Computer Science and Elect. Engineering CSET University of South Africa Johannesburg South Africa Department of Electronic and Computer Engineering Lagos State University Lagos Nigeria School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa Department of Elect. Electronic and Computer Engineering Cape Peninsula University of Technology Cape Town South Africa Department of Electrical Engineering CSET University of South Africa Johannesburg South Africa Department for Computer Science CSET University of South Africa Johannesburg South Africa

ISBN: (数字)9798350358155

ISBN: (纸本)9798350358162

This study demonstrates the utilization of Machine Learning (ML) for network slice prediction, enabling the optimization of resources for diverse network slices. Traditional methods for network slice prediction often lack efficiency and result in inaccuracies. By leveraging ML algorithms such as Naive Bayes and Random Forest, an intelligent framework that automates network slice prediction is developed. This framework enhances network virtualization and management, facilitating resource allocation. The ML algorithms take real-time network conditions and usages, such as packet delay and smart city, as input and output for selecting the most suitable network slice. Data analysis is further conducted to reveal the connections between the input parameters and how these parameters influence the selection of the accurate network slice. Network slicing plays a crucial role as it enables the customization of services and facilitates efficient scaling to meet the specific needs of different applications and industries. The accuracy scores of the employed ML algorithms were generally perfect, except for the KNN and SVM classifiers, which achieved an accuracy of 94.30% and 92.16%, respectively, for the prediction of network slices based on incoming network connections and usages.

关键词： Support vector machines Machine learning algorithms Accuracy Data analysis Smart cities Network slicing Prediction algorithms

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Quaternion Tensor Completion Via Qr Decomposition and Nuclear Norm Minimization

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SSRN 2024年

作者： Sun, Jian Liu, Xin Zhang, Yang School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology China Macau Institute of Systems Engineering Faculty of Innovation Engineering Macau University of Science and Technology China Department of Mathematics University of Manitoba Winnipeg Canada

The task of tensor (matrix) completion has been widely used in the fields of computer vision and image processing, etc. To achieve the completion, the existing methods are mostly based on singular value decomposition of the real tensor and nuclear norm minimization. However, the real tensor completion methods cannot simultaneously maintain color channel correlation and evolution robustness of color video frames, they need high computational cost to handle the high-dimensional data. Hence they have some limitations in model generalization ability and computational efficiency. In this paper, a new completion method for the quaternion tensor (matrix) is explored via the QR decomposition and the definition of novel quaternion tensor norm, which can well balance the model generalization ability and efficiency, and the performance of the completion method has been substantially improved. Numerical experiments on color images and videos prove the effectiveness of our proposed method. © 2024, The Authors. All rights reserved.

关键词： Tensors

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A Core Ontology to Support Agricultural Data Interoperability

A Core Ontology to Support Agricultural Data Interoperabilit...

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2023 Datenbanksysteme fur Business, Technologie und Web, BTW 2023 - 2023 Database systems for Business, Technology and Web, BTW 2023

作者： Abdelmageed, Aly Hatem, Shahenda Wael, Tasneem Medhat, Walaa König-Ries, Birgitta Ellakwa, Susan F. Elkafrawy, Passent Algergawy, Alsayed Information Technology and Computer Science School Nile University Egypt Heinz Nixdorf Chair for Distributed Information Systems Friedrich-Schiller University of Jena Germany Climate Change Information Center Renewable Energy Expert Systems Agricultural Research Center Egypt Collage of Engineering Effat University Saudi Arabia

ISBN: (纸本)9783885797258

The amount and variety of raw data generated in the agriculture sector from numerous sources, including soil sensors and local weather stations, are proliferating. However, these raw data in themselves are meaningless and isolated and, therefore, may offer little value to the farmer. Data usefulness is determined by its context and meaning and by how it is interoperable with data from other sources. Semantic web technology can provide context and meaning to data and its aggregation by providing standard data interchange formats and description languages. In this paper, we introduce the design and overall description of a core ontology that facilitates the process of data interoperability in the agricultural domain. © 2023 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Ontology

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Robust Authentication Protocols for IoT Devices in High-Density Networks

Robust Authentication Protocols for IoT Devices in High-Dens...

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Distributed systems, computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： Senthilkumar Thangavel Arnav Kotiyal Ancy Thomas Harshal Patil Shirish Kulkarni Natrayan L Distributed Systems Cloud Solutions & Machine Learning Expert Paypal Inc California USA Department of Computer Science & Engineering Graphic Era Dehradun India Department of Computer Science and Engineering RNSIT Bangalore India School of Computer Science & Engineering IILM University Greater Noida India Department of Law Symbiosis Law School Symbiosis International (Deemed University) Pune India Department of Mechanical Engineering Saveetha School of Engineering SIMATS Kuthambakkam India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Internet of things (IoT) gadgets have transformed several industries, including engineering, medicine, and more, thanks to the meteoric rise in the number of connected smart devices. The core principle of using the Internet of Things is that it expedites the delivery of information while consuming very little energy. In a nutshell, the Internet of Things (IoT) is a system of interconnected computing devices, services, and data that allows everyday objects to communicate with one another and with their physical environments. Prior to being saved on the server, all data sent across the IoT network must first be aggregated in the Queuing Telemetry Transport protocol (QTTP) broker. Access control, authorization, data storage, secrecy, authentication, system construction, and organization are some of the primary security concerns surrounding the Internet of Things (IoT). Three innovative security frameworks—RSA, Advanced Encryption Standard, and Elliptical Curve Cryptography—are created to enhance the protection of data in powered IoT devices. The security of IoT data is ensured by integrating all cryptic algorithms with the Constrained Queue Telemetry Transport Protocol (CQTTP). Data transmission from each IoT node undergoes cryptographic processes before being aggregated in the CQTTP. Exchange of key approvals, confirmation, and preservation of application data contrast are all accomplished by this cryptic action. Encryption keys are crucial to the safety of the data ix. The primary security component that reveals how many distinct key values a key in a protocol may take is the key's size. As a result, this study uses a new random key size method to give keys of varying sizes to the data sent by each node. Cryptography is harder when key sizes are set to be variable and random. In order to prevent script XSS commands and incorrect characteristics, all communications are encrypted using a technology that often permits employing TLS instead of plain TCP. Consequently, th

关键词： Transport protocols Wireless sensor networks Authentication Elliptic curve cryptography Encryption Internet of Things Security Telemetry Smart devices Radiofrequency identification

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Enabling Surrogate-Assisted Evolutionary Reinforcement Learning via Policy Embedding 1

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17th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2022

作者： Tang, Lan Li, Xiaxi Zhang, Jinyuan Li, Guiying Yang, Peng Tang, Ke Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Faculty of Engineering Shenzhen MSU-BIT University Shenzhen518172 China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China Department of Statistics and Data Science Southern University of Science and Technology Shenzhen518055 China

ISBN: (数字)9789819915491

ISBN: (纸本)9789819915484

Evolutionary Reinforcement Learning (ERL) that applying Evolutionary Algorithms (EAs) to optimize the weight parameters of Deep Neural Network (DNN) based policies has been widely regarded as an alternative to traditional reinforcement learning methods. However, the evaluation of the iteratively generated population usually requires a large amount of computational time and can be prohibitively expensive, which may potentially restrict the applicability of ERL. Surrogate is often used to reduce the computational burden of evaluation in EAs. Unfortunately, in ERL, each individual of policy usually represents millions of weights parameters of DNN. This high-dimensional representation of policy has introduced a great challenge to the application of surrogates into ERL to speed up training. This paper proposes a PE-SAERL Framework to at the first time enable surrogate-assisted evolutionary reinforcement learning via policy embedding (PE). Empirical results on 5 Atari games show that the proposed method can perform more efficiently than the four state-of-the-art algorithms. The training process is accelerated up to 7x on tested games, comparing to its counterpart without the surrogate and PE. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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Intelligent Soft Robotic Gripper Enabled by Multimodal Sensors and Deep Learning 18

Intelligent Soft Robotic Gripper Enabled by Multimodal Senso...

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18th International Conference on Nano/Micro Engineered and Molecular systems, NEMS 2023

作者： Shi, Qiongfeng Sun, Zhongda Le, Xianhao Xie, Jin Lee, Chengkuo Southeast University Joint International Research Laboratory of Information Display and Visualization School of Electronic Science and Engineering Nanjing China National University of Singapore Department of Electrical and Computer Engineering Singapore Singapore Zhejiang University State Key Laboratory of Fluid Power and Mechatronic Systems Hangzhou China

ISBN: (纸本)9798350333480

Here we report an intelligent soft robotic gripper enabled by the integration of an ultrasonic remote sensor and triboelectric sensors. Due to the noncontact distance sensing ability, the ultrasonic sensor is used to find the object's visual information including position and height by lateral scanning. The information is then used for adjusting the robotic gripper to an appropriate grasp location, after which grasp operation is performed to obtain the object's tactile information through triboelectric bending and tactile sensors. To efficiently analyze the multimodal information, a deep-learning neural network based on feature-level data fusion is constructed, which is able to achieve a high accuracy of 99.3% in classifying 14 objects, enabling the intelligent soft robotic gripper for various smart applications. © 2023 IEEE.

关键词： Classification (of information)

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Machine Learning Based Intelligent Routing for VDTNs

Machine Learning Based Intelligent Routing for VDTNs

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International Conference on computer Communications and Networks (ICCCN)

作者： Shiyi Liu Haiying Shen Brian L. Smith Volker Fessmann Department of Computer Science University of Virginia Department of Engineering Systems and Environment University of Virginia Federal Highway Administration

Delay Tolerant Networks (DTNs) are wireless mobile networks resilient to disruptions, significant latency, and incomplete paths between source and destination (S-D). Vehicular Delay Tolerant Network (VDTNs) is a subclass of DTNs. Most existing routing algorithms for VDTNs rely on predetermined rules that are inflexible to changes in node features (such as location, contact history, and node relationships). Moreover, given the extremely dynamic network architecture of the VDTN, the routing protocols must be flexible to the varying node features. Thus, the purpose of this work is to develop an intelligent routing system capable of determining the optimal routing method given a pair of S-D nodes. In this study, a novel self-adaptive routing method for VDTNs based on machine learning (ML) is proposed. Using real-time features of S-D nodes, the proposed method selects the optimal routing method. Using the Opportunistic Network Environment (ONE) simulator based on a real-world taxi trace, we evaluated the performance of our ML-based self-adaptive routing method employing various ML models. Evaluation results show that our method achieves up to 34.34% greater success rate and up to 23.75% lower average message delivery delay than existing methods.

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Metric Based Dynamic Control Charts for Edge Anomaly Detection in Factory Logistics

Metric Based Dynamic Control Charts for Edge Anomaly Detecti...

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2022 International Conference on Control Theory and Applications, ICoCTA 2022

作者： Eschemann, Patrick Borchers, Philipp Lisiecki, Dennis Krauskopf, Jan Elmar OFFIS Institute for Computer Science Germany DLR e.V. Institute Systems Engineering for Future Mobility Germany Carl von Ossietzky Universität Oldenburg Germany

The optimization of transport logistics in production environments is a holistic task for the factory of the future. Autonomous guided vehicles that perform transport jobs in factories are facing this challenge and have to detect, react and prepare to unforeseen changes and anomalies in the production system. Due to data protection concerns, details like production plans are often not available for an external transportation system. Hence the anomaly detection has to be based on self-collected and observed data of the transport system like occurred transport needs or the evolution of internal metrics. In this paper we infused a production system with manufacturing process anomalies and demonstrate a detection based on the observation of transport needs to overcome the gab caused by restricted information. For that detection we extended classic control charts to work with expected values based on learned dynamic production characteristics. The system sets a tolerance field as narrow as possible around dynamically determined values, resulting in an average precision of 95% for detection unusual number of transport jobs. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Control charts

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Paddy Plant Disease Detection using Hybrid Deep Learning Algorithms

Paddy Plant Disease Detection using Hybrid Deep Learning Alg...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： R Suguna S Deepa T Kanimozhi M Chandru G Arun Harshini M Department of Computer Science and Engineering Bannari Amman Institute of Technology Sathyamangalam Erode Tamilnadu India Department of Computer Technology - PG Kongu Engineering College Perundurai Erode Tamilnadu Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore Department of Computer Science VET-Institute of Arts and Science (Co-Education) College Erode Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai M.Sc (Software Systems) Kongu Engineering College Perundurai Erode Tamilnadu

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

Agriculture is the backbone of India’s economy, supporting a large portion of the population and establishing the country as a key global player in food production. India ranks as the second-largest producer of both paddy and wheat. However, agricultural productivity is highly susceptible to various plant diseases, which can significantly reduce crop yields and quality. Paddy, one of the most important staple crops, is particularly vulnerable to diseases caused mainly by viral and bacterial related diseases. The climate conditions are also play a vital role in paddy plant diseases. This research work proposes a deep learning-based approach for early detection and classification of paddy diseases. The proposed research devises a hybrid model that combines the VGG19 and ResNet50 algorithms, the system analyses images of both healthy and disease-affected paddy leaves, sourced from the Kaggle repository. This research focuses on predicting four major paddy diseases that can cause yield losses of up to 80%, and in severe cases, can destroy an entire crop: (i) Paddy Blast, (ii) Bacterial Leaf Blight, (iii) Sheath Blight, and (iv) Ufra Disease. The proposed model achieves a high accuracy rate of 98% in predicting paddy diseases and offers a rapid classification process, showcasing the potential of advanced image processing and deep learning techniques to mitigate the impact of paddy plant diseases on agricultural productivity.

关键词： Deep learning Productivity Plant diseases Microorganisms Accuracy Prediction algorithms Real-time systems Classification algorithms System analysis and design Residual neural networks

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Ovarian Cancer Prediction using Machine Learning

Ovarian Cancer Prediction using Machine Learning

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Yashika Agnihotri Tarun Jain Vijay Shankar Sharma Department of Computer Science and Engineering Manipal University Jaipur Department of IOT & Intelligent Systems Manipal University Jaipur

Ovarian cancer is the type of cancer that has the highest recurrence rate in women and poses a serious threat to women. Due to the lack of observable signs, this quiet invader often goes undetected in the beginning, leaving women susceptible to its sneaky development. Once it has advanced and its symptoms are obvious, it becomes arduous to treat. Machine learning is emerging as a ray of hope in the healthcare scene in the current era of rapid technological innovation. Machine learning provides a promising path for the early diagnosis and prediction of ovarian cancer by harnessing the power of algorithms and data analytics, perhaps turning the tide against this tough enemy. The dataset [17] comprises of 349 instances and 49 features. The goal here is to derive a model that helps in classification of instances into two classes: Benign Ovarian Tumors (BOT) and Ovarian Cancer (OC). Therefore, methodical machine learning algorithms are employed to detect ovarian cancer. Our research findings revealed that out of all the classifiers, the Random Forest Model has the highest accuracy (91.43 %). This approach is capable of accurately classifying BOT and OC and could have immense effect on diagnosis of patients, leading to better treatments and higher survival rate.

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