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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Snehal Hole Abdul Razzaq T. Zaboun Hayder Al-Ghanimi Omad Aldeen Hussen Haider Alchilibi Sahar R. Abdul Kadeem S.N.G Institute of Management and Research Pune India Forensic Sciences Department Al-Esraa University College Baghdad Iraq Department of Medical Instrumentation Techniques Engineering Hilla University College Babylon Iraq Computer Engineer Department Al-Kunooze University College Basra Iraq Medical Laboratory Techniques Department Al-Farahidi University Baghdad Iraq National University of Science and Technology Dhi Qar Iraq

The Underwater Wireless Sensor Network (UWSN) is a collection of sensor nodes deployed beneath the seabed to monitor and record challenging applications in offshore exploration and oceanic environments. In order to ensure proper energy consumption in a dynamic environment, the effective forwarding of data packets through efficient paths is crucial. Routing, therefore, plays a significant role in reducing power consumption by making informed decisions on packet destinations. In this study, we propose and analyze an efficient routing technique for underwater wireless sensor networks based on Grey-Wolf Optimization. The proposed technique is evaluated under various conditions, and the experimental results demonstrate a reduced number of iterations for selecting a cluster head, leading to a longer network lifetime compared to existing protocols such as Energy Efficient Routing Protocol (EERP) and Directional Flooding based Routing (DFR).

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An Intelligent Approach to Segment the Liver Cancer using Machine Learning Method

An Intelligent Approach to Segment the Liver Cancer using Ma...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： L Anand Mahesh Maurya J. Seetha D Nagaraju Ananda Ravuri R G Vidhya Department of networking and communications School of computing SRM Institute of science and Technology K. C. College of Engineering Management and Research University of Mumbai Mith Bunder Road Near Hume Pipe Thane Maharashtra India Department of Computer Science and Business Systems Panimalar Engineering College Chennai Department of CSE Sri Venkatesa Perumal College of Engineering and Technology Puttur Andhra Pradesh India Senior Software Engineer Intel corporation Hillsboro Oregon USA Department of ECE HKBK College of Engineering Bangalore

Cancer Liver cancer is difficult to detect and diagnose early. Machine learning has improved liver cancer diagnosis in recent years. This abstract describes a novel liver cancer detection and classification method using Autoencoder-Extreme Learning Machine (AE-ELM) and Convolutional Neural Network (CNN) technology. AE-ELM models extract features in the suggested method. The autoencoder-extreme learning machine AE-ELM technique learns high-level abstract characteristics from incoming data. AE-ELM extracts liver cancer-specific features by reducing the dimensionality of input data. A CNN model classifies the extracted features. CNNs are good at detecting spatial patterns in medical pictures like CT and MRI scans. The CNN model learns hierarchical representations of input information and classifies them into liver cancer subtypes or stages using various convolutional and pooling processes. AE-ELM and CNN are advantageous. First, AE-ELM decreases input data dimensionality to extract critical features without manual feature engineering. This simplifies feature selection and improves the model’s capacity to handle big data. Second, the CNN model uses hierarchical convolutional processes to capture subtle patterns and spatial correlations in input data, enhancing liver cancer classification accuracy. The proposed liver cancer diagnostic method works in experiments. AE-ELM and CNN models outperform classic machine learning approaches and standalone CNN models. Improved liver cancer detection accuracy, sensitivity, and specificity help healthcare practitioners make quick and precise diagnosis. Finally, AE-ELM and CNN technologies can identify and classify liver cancer. This unique strategy uses both models to extract relevant characteristics and capture complicated patterns, improving liver cancer diagnosis accuracy and efficiency. To evaluate this approach’s therapeutic promise and application in real-world healthcare settings, additional datasets are needed.

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Sim2Real Grasp Pose Estimation for Adaptive Robotic Applications

arXiv

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arXiv 2022年

作者： Horváth, Dániel Bocsi, Kristóf Erdős, Gábor Istenes, Zoltán Centre of Excellence in Production Informatics and Control Institute for Computer Science and Control Eötvös Loránd Research Network Budapest Hungary CoLocation Center for Academic and Industrial Cooperation Eötvös Loránd University Budapest Hungary Department of Manufacturing Science and Engineering Budapest University of Technology and Economics Budapest Hungary

Adaptive robotics plays an essential role in achieving truly co-creative cyber physical systems. In robotic manipulation tasks, one of the biggest challenges is to estimate the pose of given workpieces. Even though the recent deep-learning-based models show promising results, they require an immense dataset for training. In this paper, two vision-based, multi-object grasp pose estimation models (MOGPE), the MOGPE Real-Time and the MOGPE High-Precision are proposed. Furthermore, a sim2real method based on domain randomization to diminish the reality gap and overcome the data shortage. Our methods yielded an 80% and a 96.67% success rate in a real-world robotic pick-and-place experiment, with the MOGPE Real-Time and the MOGPE High-Precision model respectively. Our framework provides an industrial tool for fast data generation and model training and requires minimal domain-specific data. © 2022, CC BY-NC-ND.

关键词： Cyber Physical System

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DVO + LCLMF: A web service recommendation mechanism with QoS privacy preservation

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Concurrency and Computation: Practice and Experience 2022年第18期34卷 e7003-e7003页

作者： Li, Kui Ji, Yi-mu Liu, Shang-dong Wu, Fei Yao, Hai-chang He, Jing Liu, Qiang Liu, Yan-lan Shao, Si-si You, Shuai School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Nantong Bigdata Research Key Laboratory for Rehabilitation Medicine The Second People's Hospital of Nantong Nantong China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing China Institute of High Performance Computing and Bigdata Nanjing University of Posts and Telecommunications Nanjing China Jiangsu HPC and Intelligent Processing Engineer Research Center Nanjing China School of Software and Electrical Engineering Swinburne University of Technology MelbourneVIC Australia

QoS-aware based web service recommendation is one of the crucial solutions to help users find high-quality web services. To accurately predict the QoS values of candidate services, it is usually required to collect historical QoS data of users (QoS data for short). If these collected QoS data are improperly processed, QoS data privacy may be threatened. However, how to accurately predict the QoS values of candidate services while protecting QoS data privacy has not been well studied. In response to the situation, we propose a hybrid web service recommendation mechanism, which is divided into three parts. In the first part, the QoS data privacy preservation algorithm, which called DVO, is proposed based on keeping the cosine similarity of QoS data unchanged, that is, to realize the confusion of QoS data while ensuring the availability of QoS data remains unchanged. In the second part, a hybrid matrix factorization model based on location information and service features, which called LCLMF, is proposed to improve the accuracy of QoS values prediction. According to DVO and LCLMF, the DVO + LCLMF is designed in the third part, which can accurately predict QoS values while protecting QoS data privacy. The experimental results show that DVO + LCLMF can accurately predict the QoS values of candidate services on the basis of attaining QoS data privacy protection. © 2022 John Wiley & Sons, Ltd.

关键词： Data privacy

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SmartSantander: IoT Experimentation over a Smart City Testbed

arXiv

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arXiv 2024年

作者： Sanchez, Luis Muñoz, Luis Galache, Jose Antonio Sotres, Pablo Santana, Juan R. Gutierrez, Veronica Ramdhany, Rajiv Gluhak, Alex Krco, Srdjan Theodoridis, Evangelos Pfisterer, Dennis University of Cantabria Plaza de la Ciencia s/n Santander39005 Spain Lancaster University South Drive LancasterLA1 4WA United Kingdom University of Surrey Faculty of Engineering & Physical Sciences GuildfordGU2 7XH United Kingdom Ericsson Serbia Milentija Popovica 5a/V Belgrade11070 Serbia Research Academic Computer Technology Institute Patras26500 Greece University of Lübeck Ratzeburger Allee 160 Lúbeck23562 Germany

This paper describes the deployment and experimentation architecture of the Internet of Things experimentation facility being deployed at Santander city. The facility is implemented within the SmartSantander project, one of the projects of the Future Internet research and Experimentation initiative of the European Commission and represents a unique in the world city-scale experimental research facility. Additionally, this facility supports typical applications and services of a smart city. Tangible results are expected to influence the definition and specification of Future Internet architecture design from viewpoints of Internet of Things and Internet of Services. The facility comprises a large number of Internet of Things devices deployed in several urban scenarios which will be federated into a single testbed. In this paper the deployment being carried out at the main location, namely Santander city, is described. Besides presenting the current deployment, in this article the main insights in terms of the architectural design of a large-scale IoT testbed are presented as well. Furthermore, solutions adopted for implementation of the different components addressing the required testbed functionalities are also sketched out. The IoT experimentation facility described in this paper is conceived to provide a suitable platform for large scale experimentation and evaluation of IoT concepts under real-life conditions. © 2024, CC BY-NC-ND.

关键词： Testbeds

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Image Processing Approaches for Oral Cancer Detection in Color Images

Image Processing Approaches for Oral Cancer Detection in Col...

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

作者： Amarjeet Singh T Ch Anil Kumar Tiruvedula Mithun Sankararao Majji Mooda Rajesh Palagati Anusha Computer Science and Engineering New Horizon College of Engineering Bengaluru Karnataka India Vignan's Foundation for Science Technology and Research Guntur Andhra Pradesh India Senior Software Engineer Leanovate Info Solutions Benguluru Karnataka India Department of ECE GRIET HyderabadIndia Mallareddy Institute of Technology and Science Maisammaguda Telangana India Computer Science and Engineering KMM Institute of Technology and Science Tirupati Andhra Pradesh India

ISBN: (纸本)9781665435253

Lips, two-thirds of the tongue and inner cheek lining are all common places for oral cancer to form. It can also arise in hard and soft palates, pharynx, and sinuses. Head and neck cancers are subtypes of this malignancy. Unless detected and treated early on, oral can be dangerous. Using microscopic biopsy images, the researchers were able to detect mouth cancer and non-cancerous lesions. Using cutting-edge techniques, it is possible to histologically diagnose oral lesions. For example, enhancing microscopic images involves transforming them from RGB to L*a*b color space, classifying the colors using k-means clustering, segmenting the nuclei, and obtaining and classifying their features.

关键词： Tongue Image color analysis Lips Biopsy Mouth Neck Lesions

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Lane Marking Detection Algorithm Based on High-Precision Map 6th

Lane Marking Detection Algorithm Based on High-Precision Map

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6th International Conference on Data Science, ICDS 2019

作者： Yao, Haichang Chen, Chen Liu, Shangdong Li, Kui Ji, Yimu Wang, Ruchuan School of Computer and Software Nanjing Institute of Industry Technology Nanjing210023 China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Institute of High Performance Computing and Big Data Nanjing University of Posts and Telecommunications Nanjing210003 China Nanjing Center of HPC China Nanjing210003 China Jiangsu HPC and Intelligent Processing Engineer Research Center Nanjing210003 China

ISBN: (纸本)9789811528095

In case of sharp road illumination changes, bad weather such as rain, snow or fog, wear or missing of the lane marking, the reflective water stain on the road surface, the shadow obstruction of the tree, and mixed lane markings and other signs, missing detection or wrong detection will occur for the traditional lane marking detection algorithm. In this manuscript, a lane marking detection algorithm based on high-precision map is proposed. The basic principle of the algorithm is to use the centimeter-level high-precision positioning combined with high-precision map data to complete the detection of lane markings. The experimental results show that the algorithm has lower false detection rate in case of bad road conditions, and the algorithm is robust. © Springer Nature Singapore Pte Ltd 2020.

关键词： Road and street markings

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An Improved Spectral Clustering Algorithm Using Fast Dynamic Time Warping for Power Load Curve Analysis 11th

An Improved Spectral Clustering Algorithm Using Fast Dynamic...

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11th International Conference on Mobile Computing, Applications, and Services, MobiCASE 2020

作者： Bi, Zhongqin Leng, Yabin Liu, Zhe Li, Yongbin Fuentes, Stelios College of Computer Science and Technology Shanghai University of Electric Power Shanghai China State Grid Shanghai Electric Power Research Institute Shanghai China Office of Academic Affairs Shanghai University of Electric Power Shanghai China Leicester University Leicester United Kingdom

ISBN: (纸本)9783030642136

Cluster analysis of power loads can not only accurately extract the commonalities and characteristics of the loads, but also help to understand the users’ habits and patterns of electricity consumption, so as to optimize the power dispatching and regulate the operation of the entire power grid. Based on the traditional clustering methods, this paper proposes a clustering algorithm that can automatically determine the optimal cluster number. Firstly, Fast-DTW algorithm is used as the similarity measuring function to calculate the similar matrix between two time series, and then Spectral Clustering and Affinity Propagation (AP) algorithm are used for clustering. It is combined with Euclidean distance, DTW and Fast-DTW algorithms to evaluate the algorithm effect. By analyzing the actual power data, our results show that the improved external performance evaluation index ARI, AMI and internal performance evaluation index SSE are significantly improved and have better time series similarity and accuracy. Applying the algorithm to more than six thousands of users, twelve kinds of typical power load patterns can be obtained. For any other load curve, it can be mapped to a standard load by feature extraction. The corresponding prediction model is adopted, which is of great significance to reduce the peak power consumption, adjust the electricity price appropriately and solve the problem of system balance. © 2020, ICST institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Time series

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Finding shortest and nearly shortest path nodes in large substantially incomplete networks

arXiv

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arXiv 2022年

作者： Kitsak, Maksim Ganin, Alexander Elmokashfi, Ahmed Cui, Hongzhu Eisenberg, Daniel A. Alderson, David L. Korkin, Dmitry Linkov, Igor Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Network Science Institute Northeastern University BostonMA02115 United States Department of Systems and Information Engineering University of Virginia CharlottesvilleVA22904 United States U.S. Army Engineer Research and Development Center Contractor Concord MA01742 United States Simula Metropolitan Center for Digital Engineering Oslo Norway Bioinformatics and Computational Biology Program Worcester Polytechnic Institute WorcesterMA01609 United States Institute for Genomic Medicine Columbia University Medical Center New YorkNY United States Operations Research Department Naval Postgraduate School MontereyCA93943 United States Data Science Program Worcester Polytechnic Institute WorcesterMA01609 United States Computer Science Department Worcester Polytechnic Institute WorcesterMA01609 United States U.S. Army Engineer Research and Development Center Environmental Laboratory Concord MA01742 United States

Dynamic processes on networks, be it information transfer in the Internet, contagious spreading in a social network, or neural signaling, take place along shortest or nearly shortest paths. Unfortunately, our maps of most large networks are substantially incomplete due to either the highly dynamic nature of networks, or high cost of network measurements, or both, rendering traditional path finding methods inefficient. We find that shortest paths in large real networks, such as the network of protein-protein interactions (PPI) and the Internet at the autonomous system (AS) level, are not random but are organized according to latent-geometric rules. If nodes of these networks are mapped to points in latent hyperbolic spaces, shortest paths in them align along geodesic curves connecting endpoint nodes. We find that this alignment is sufficiently strong to allow for the identification of shortest path nodes even in the case of substantially incomplete networks. We demonstrate the utility of latent-geometric path finding in problems of cellular pathway reconstruction and communication security. Copyright © 2022, The Authors. All rights reserved.

关键词： Proteins

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Designing an Educational Chatbot: A Case Study of CikguAIBot 5

Designing an Educational Chatbot: A Case Study of CikguAIBot

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5th International Conference on Information Retrieval and Knowledge Management, CAMP 2021

作者： Nasharuddin, Nurul Amelina Sharef, Nurfadhlina Mohd Mansor, Evi Indriasari Samian, Normalia Murad, Masrah Azrifah Azmi Omar, Mohd Khaizer Arshad, Noreen Izza Shahbodin, Faaizah Marhaban, Mohammad Hamiruce Universiti Putra Malaysia Upm Serdang Faculty of Computer Science and Information Technology Selangor Malaysia Universiti Putra Malaysia Upm Serdang Faculty of Educational Studies Selangor Malaysia Universiti Teknologi Petronas Institute of Autonomous Systems Positive Computing Research Center Bandar Seri Iskandar Perak Malaysia Universiti Teknikal Malaysia Melaka Centre for Academic Excellence and Scholarship Hang Tuah Jaya Melaka Malaysia Vice Chancellor Office Universiti Sains Islam Malaysia Bandar Baru Nilai Negeri Sembilan Malaysia

ISBN: (纸本)9781665412377

This research aims to design a chatbot application to teach Artificial Intelligent (AI) in Malay language. CikguAIBot offers learners the possibility to learn and interact with an agent without the need for a teacher to be around. The development of CikguAIBot is based on the RAD model with the involvement of a number of experts and real users. The main focus of this paper is on the contents and flow design of the chatbot so that the objectives of the chatbot are achieved. Results from the expert review sessions were reported and a detailed evaluation strategy with the students is also included although the evaluation session is in the future plan. This research is expected to foster the usage of chatbot technology in supporting successful learning in Malaysia's education system. © 2021 IEEE.

关键词： Education computing

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