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Advancing Service Composition Platforms: A Context-Aware and Blockchain EnhancedFramework for Trusted Service Process Creation and Recommendation

Advancing Service Composition Platforms: A Context-Aware and...

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2024 International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Bhardwaj, Mukesh Kumar Singh, Mahesh Kumar Baghel, Deepak Pal, Aditya Kumar, Angad Department of Computer Science and Engineering Dronacharya Group of Institutions Uttar Pradesh Greater Noida India Department of Computer Science and Information Technology Dronacharya Group of Institutions Uttar Pradesh Greater Noida India Department of Information Technology Dronacharya Group of Institutions Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350355338

In service computing, platforms like IFTTT, Zapier, and Omelette enable users to create customized workflows. However, this flexibility brings challenges like security, trust, and efficiency. Recommending components and workflows is also difficult due to concerns about data integrity, service reliability, and the limitations of traditional recommendation *** this groundbreaking study, we unveil the ContextAware Responsible Service Process Creation and Recommendation (SPCR-CA) framework, a revolutionary approach leveraging blockchain forrobust service provisioning and harnessing the power of Recurrent Neural Network (RNN) and Skip-Gram models for intelligent service recommendations. SPCR-CA is dedicated to elevating trust andfostering reusability within service processes through its blend of secure, transparent, and efficient composition techniques. Through meticulous experimentation with ProgrammableWeb datasets, SPCR-CA emerges triumphant, showcasing unparalleled performance in recommending service components, promising to revolutionize efficiency, security, and user satisfaction across composition platforms. This endeavour not only confronts present-day obstacles head-on but also sets the stage for pioneering exploration in the realm of service computing. © 2024 IEEE.

关键词： Efficiency

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Classification of Nutritional Deficiencies in Cabbage Leave Using Random Forest 11

Classification of Nutritional Deficiencies in Cabbage Leave ...

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11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Chauhan, Nupa Ram Shukla, Ratnesh Kumar Sengar, Alok Singh Gupta, Anurag College of Computing Science and Information Technology Computer Science and Engineering UP Moradabad India Shambhunath Institute of Engineering and Technology Computer Science and Engineering UP Prayagraj India

ISBN: (纸本)9781665487320

Now a day agriculture is very important in India since it is a growing nation. But generally the crop production attained by farmers would be much below the optimal production. It is very important to correctly detecting and identifying the crop diseases to enhance the profit of the formers and the stakeholder. The main reason for the crop production gap is due to the lack of essential soil nutrients and irrigation in the agricultural farms. To escalate the crop production, it is essential to balance the chemical elements or nutrients present in the soil with varying parameters of soil like the pH and soil moisture. Crop productivity can be increased to optimum level by efficient soil nutrient management. In case of Nutrient deficiencies, visual symptoms will appear on the leaf. This paper put forwards a method to identify the nutrient deficiencies of plants by making use of visual symptoms appearing on the leaves by Classification. Eight types of deficiencies i.e. N, P, K, Ca, B, Zn and Mg will be studied. The proposed study consists of creation and pre¬processing of a set of images consisting of nutrient deficient and healthy leaves, feature extraction and by using Random Forest performing multi class classification of nutrient deficient leaves. Evaluation of tomato leaf from the dataset focuses on recognizing the visual detection and indications of nutritional deficiencies. The proposed architecture achieves the 98.30% accuracy with the model size of 9.20 MB. © 2022 IEEE.

关键词： Nutrients

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Improved Streaming Algorithm for Minimum Cost Submodular Cover Problem 12th

Improved Streaming Algorithm for Minimum Cost Submodular Co...

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12th International Conference on Computational Data and Social Networks, CSoNet 2023

作者： Tran, Tan D. Pham, Canh V. Trung, Dung P. Nguyen, Uyen T. Faculty of Information Technology VNU University of Engineering and Technology Hanoi Viet Nam ORLab Faculty of Computer Science Phenikaa University Hanoi Viet Nam Institute of Engineering and Technology Vinh University Nghean Viet Nam

ISBN: (纸本)9789819706686

This paper introduces an efficient streaming algorithm for a well-known Minimum cost Submodular Cover (MSC) problem. Our algorithm makes O(logn) passes over the ground set, takes O(nlogn) query complexity and returns a (1/ϵ,1-ϵ)-bicriteria approximation solution with the ground set of size n and the precise parameter ϵ>0. Therefore, it is better than the existing streaming algorithms in terms of both solution guarantees and query complexity. Besides the theoretical performance, the experiment results on two applications, Revenue Threshold and Coverage Threshold, further show the superiority of our algorithm with the state-of-the-art ones. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Approximation algorithms

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Optimal Load Forecasting Model for Peer-to-Peer Energy Trading in Smart Grids

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computers, Materials & Continua 2022年第1期70卷 1053-1067页

作者： Lijo Jacob Varghese K.Dhayalini Suma Sira Jacob Ihsan Ali Abdelzahir Abdelmaboud Taiseer Abdalla Elfadil Eisa Department of Electrical and Electronics Engineering Christian College of Engineering and TechnologyOddanchatram624619India Department of Electrical and Electronics Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science and Engineering Christian College of Engineering and TechnologyOddanchatram624619India Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversity of Malaya50603Kuala LumpurMalaysia Department of Information Systems King Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Information Systems-Girls Section King Khalid UniversityMahayil62529Saudi Arabia

Peer-to-Peer(P2P)electricity trading is a significant research area that offers maximum fulfilment for both prosumer and *** also decreases the quantity of line loss incurred in Smart Grid(SG).But,uncertainities in demand and supply of the electricity might lead to instability in P2P market for both prosumer and *** recent times,numerous Machine Learning(ML)-enabled load predictive techniques have been developed,while most of the existing studies did not consider its implicit features,optimal parameter selection,and prediction *** order to overcome fulfill this research gap,the current research paper presents a new Multi-Objective Grasshopper Optimisation Algorithm(MOGOA)with Deep Extreme Learning Machine(DELM)-based short-term load predictive technique i.e.,MOGOA-DELM model for P2P Energy Trading(ET)in *** proposed MOGOA-DELM model involves four distinct stages of operations namely,data cleaning,Feature Selection(FS),prediction,and parameter *** addition,MOGOA-based FS technique is utilized in the selection of optimum subset of ***,DELM-based predictive model is also applied in forecasting the load *** proposed MOGOA model is also applied in FS and the selection of optimalDELM parameters to improve the predictive *** inspect the effectual outcome of the proposed MOGOA-DELM model,a series of simulations was performed using UK Smart Meter *** the experimentation procedure,the proposed model achieved the highest accuracy of 85.80%and the results established the superiority of the proposed model in predicting the testing data.

关键词： Peer to Peer energy trade smart grid load forecasting machine learning feature selection

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Ontology-Based Data Modeling for NoSQL Databases: A Case Study in e-Healthcare Application

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SN computer science 2023年第1期4卷 1-19页

作者： Sen, Poly Sil Mukherjee, Nandini Department of Information Technology Techno Main Salt Lake Kolkata India Department of Computer Science and Engineering Jadavpur University Kolkata India

With the advent of e-healthcare applications, managing health data manually is no longer possible. Health data being voluminous, complex in nature and generally described with specialized terminologies, requires to be specified with standards like ISO EN13606RM, openEHR, HL7, etc. for reference implementation and data storage. These standards are not completely interoperable and cannot fit to sensor data, cloud and database implementation for various requirements. Thus, a generic ontology is necessary to accommodate basic clinical care. This paper first proposes one such ontology and describes the process of development of the ontology as suggested in the literature. Next, the paper also proposes an algorithm which uses a domain ontology and a set of queries to output the design of a NoSQL data store. The ontology developed in the paper is used by the proposed algorithm to output a NoSQL design for storing semi-structured and unstructured healthcare data. The performance of the algorithm is also analyzed theoretically using the NoSQL database schema generated for healthcare domain. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Big data e-Health data e-Health sensor Healthcare ontology NoSQL data model

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Design and Implementation of an Artificial Intelligence of Things-Based Autonomous Mobile Robot System for Cleaning Garbage

Design and Implementation of an Artificial Intelligence of T...

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作者： Chen, Liang-Bi Huang, Xiang-Rui Chen, Wei-Han Pai, Wen-Yu Huang, Guan-Zhi Wang, Wei-Chien National Penghu University of Science and Technology Department of Computer Science and Information Engineering Magong Penghu880011 Taiwan The University of Sydney Faculty of Engineering and Information Technologies SydneyNSW2006 Australia

This article proposes an autonomous mobile robot (AMR) system based on the artificial intelligence of things (AIoT) for collecting garbage. The proposed system consists of an AMR subsystem, a robot operating system (ROS) robot subsystem, an artificial intelligence (AI) garbage recognition subsystem, a solar power supply subsystem, and a smart trash can subsystem. The AMR subsystem uses a motor drive, which is built on an aluminum alloy metal base and is connected to an AI edge computing module via an app, to control the robot. The ROS subsystem is built on an edge computing module, which is coupled with the motor drive module to control the robot arm and the robot gripper and is connected to the AI edge computing module to control the robot arm through the app. The AI garbage recognition subsystem recognizes the types of waste in real time through an image sensor (camera). This subsystem adopts multiple sensors that can monitor the level of waste in the trash can. Moreover, a map on a web platform locates all trash cans. Servomotors enable automatic opening and closing of the lid to prevent the unpleasant smell of garbage from wafting. A user connects to the AI edge computing module through an app to monitor the robot and control the movement of the robot to any location on the map. © 2001-2012 IEEE.

关键词： Edge computing

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Blockchain Enabled Intelligent Surveillance System Model with AI and IoT 8

Blockchain Enabled Intelligent Surveillance System Model wit...

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8th IEEE International Conference on science, technology, engineering and Mathematics, ICONSTEM 2023

作者： Sathyaseelan, K. Vyas, Tushar Madala, Ravikiran Chamundeeswari, Vijaya Rai Goyal, Himanshu Jayaraman, Raja Department of Information Technology Mallasamudram India Jecrc University Department of Computer Science and Engineering Rajasthan Jaipur India University of Cumberlands Ph.D Information Technology DurhamNC United States Saveetha Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Graphic Era Deemed to Be University Department of Computer Science Engineering Uttrakhand Dehradun India St. Martin's Engineering College Department of Computer Science and Engineering Secunderabad India

ISBN: (纸本)9798350347791

In the New Year, Internet of Things (IoT) is industrializing in a few certifiable applications, for example, clever transportation, brilliant city to make human existence reliable. Extreme amounts of detecting information are being delivered from various sensors devices in the Modern IoT as a result of the growing industrialization of IoT. Understanding the constraints of flow innovations and the potential for future exploration techniques is necessary to the establishment of a secure and dependable information environment. A dishonest individual can connect with a trusted person through the blockchain, a decentralized digital ledger that tracks the conversation from start to finish. This report urges the medical care industry to deploy blockchain-coordinated digital protection in order to preserve clinically relevant things in light of the use of artificial intelligence in clinical settings. Applications based on blockchain may be able to precisely pinpoint the most serious, potentially dangerous mistakes in the medical sector. Blockchain-based decentralized information insurance safeguards patient health records from data theft. © 2023 IEEE.

关键词： Blockchain

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Enhancing UAV Coverage Path Planning via Deep Reinforcement learning with Intrinsic Curiosity 10

Enhancing UAV Coverage Path Planning via Deep Reinforcement ...

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10th IEEE Smart World Congress, SWC 2024

作者： Zhou, Yinxin Chen, Yanxi Wang, Renyu Huang, Haiping Liu, Shenghao Deng, Xianjun Nanjing University of Posts and Telecommunications School of Computer Science Nanjing China Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan China Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan China

ISBN: (纸本)9798331520861

Unmanned Aerial Vehicles (UAVs) are widely used in various fields due to their agility and versatility, but their limited energy supply necessitates efficient Coverage Path Planning (CPP). Traditional CPP methods are effective in structured environments but often fall short in more complex scenarios. This paper proposes a novel approach integrating advanced reinforcement learning techniques to enhance UAV path planning. Our method combines Double Deep Q-Networks (DDQN) with Long Short-Term Memory (LSTM) networks to improve decision-making and temporal information processing capabilities. Additionally, we incorporate an Intrinsic Curiosity Module (ICM) to drive exploration and Prioritized Experience Replay (PER) to focus on high-value experiences, accelerating learning and enhancing performance. The proposed framework demonstrates superior coverage efficiency and adaptability in simulations, achieving higher coverage ratios and reduced redundant paths compared to traditional methods. These innovations provide a robust and flexible solution for autonomous UAV navigation, offering a solid foundation for future research in CPP optimization. © 2024 IEEE.

关键词： Deep reinforcement learning

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A Transfer Learning Approach for Neurodegenerative Disease Classification from Brain MRI Images: Distinguishing Alzheimer's, Parkinson's, and Control Cases 6

A Transfer Learning Approach for Neurodegenerative Disease C...

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6th International Conference on Electrical engineering and information and Communication technology, ICEEICT 2024

作者： Siddiqua, Ayesha Oni, Atib Mohammad Miah, Md. Jarez Northern University Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh Brac University Department of Electrical & Electronic Engineering Dhaka Bangladesh Institute of Information and Communication Technology Bangladesh University of Engineering and Technology Dhaka Bangladesh

ISBN: (纸本)9798350385779

Like many other critical medical conditions, different neurogenerative diseases, including Alzheimer's and Parkinson's diseases, need to get diagnosed in the primary stage. Deep learning algorithms show excellent performance in detecting neurodegenerative diseases from images obtained by magnetic resonance imaging (MRI) of the human brain. Recently, transfer learning has also shown promising outcomes in the classification of neurological conditions using brain MRI data. Here, we examine the efficacy of the transfer learning strategy utilizing four distinct CNN architectures, namely EfficientNetB0, ResNet50, InceptionV3,and Xception. Used dataset for the study has three classes;Parkinson's disease (PD), Alzheimer's disease (AD), and control (healthy). The study compares the accuracy, precision, recall, and F1- score metrics for the investigated CNN models. The result demonstrates that the EfficientNetB0 model shows the best training and testing accuracy, reaching an accuracy as high as 99.4%. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Optimal Deep Learning Based Intruder Identification in Industrial Internet of Things Environment

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computer Systems science & engineering 2023年第9期46卷 3121-3139页

作者： Khaled M.Alalayah Fatma S.Alrayes Jaber S.Alzahrani Khadija M.Alaidarous Ibrahim M.Alwayle Heba Mohsen Ibrahim Abdulrab Ahmed Mesfer Al Duhayyim Department of Computer Science College of Science and ArtsSharurahNajran UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

With the increased advancements of smart industries,cybersecurity has become a vital growth factor in the success of industrial *** Industrial Internet of Things(IIoT)or Industry 4.0 has revolutionized the concepts of manufacturing and production *** industry 4.0,powerful IntrusionDetection Systems(IDS)play a significant role in ensuring network *** various intrusion detection techniques have been developed so far,it is challenging to protect the intricate data of *** is because conventional Machine Learning(ML)approaches are inadequate and insufficient to address the demands of dynamic IIoT ***,the existing Deep Learning(DL)can be employed to identify anonymous ***,the current study proposes a Hunger Games Search Optimization with Deep Learning-Driven Intrusion Detection(HGSODLID)model for the IIoT *** presented HGSODL-ID model exploits the linear normalization approach to transform the input data into a useful *** HGSO algorithm is employed for Feature Selection(HGSO-FS)to reduce the curse of ***,Sparrow Search Optimization(SSO)is utilized with a Graph Convolutional Network(GCN)to classify and identify intrusions in the ***,the SSO technique is exploited to fine-tune the hyper-parameters involved in the GCN *** proposed HGSODL-ID model was experimentally validated using a benchmark dataset,and the results confirmed the superiority of the proposed HGSODL-ID method over recent approaches.

关键词： Industrial IoT deep learning network security intrusion detection system attribute selection smart factory

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