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检索条件"任意字段=1st International Conference on Smart Systems and Data Science, ICSSD 2019"

共 1975 条记录，以下是1-10 订阅

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Certificateless Multi -Replica data Integrity Auditing for Shared data in Cloud-Based Disaster Resilience systems 1

Certificateless Multi -Replica Data Integrity Auditing for S...

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1st international conference on Innovative Sustainable Technologies for Energy, Mechatronics and smart systems, IstEMS 2024

作者： Kumar, Deepansh Giri, Aaryaman Srivastava, Akhilesh Kumar Abes Engineering College Department of Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798350384246

In the context of Disaster Resilience systems(DRS), where data integrity and availability are paramount, cloud storage plays a crucial role. However, security concerns persist, necessitating innovative approaches. In the context of the DRS framework, this paper presents a novel Certificateless Multi-Replica Public Auditing Scheme designed for shared data in cloud storage. The suggested solution handles security concerns without adding to the workload associated with key escrow problems or certificate administration. This technique allows for secure user withdrawal from user group. It is shown that this scheme is secure against three various adversaries, defined in this paper, under common hardness assumptions by a thorough security study. Performance evaluations shows that this scheme is efficient and is also practical, positioning it as a viable solution for ensuring data integrity and availability in DRS. © 2024 IEEE.

关键词： Cloud storage

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15th international Workshop on Engineering Service-Oriented Applications and Cloud Services, WESOACS 2019, the 4th international Workshop on Adaptive Service-Oriented and Cloud Applications, ASOCA 2019, the 4th international IoT systems Provisioning and Management for Context-Aware smart Cities, ISYCC 2019, the 1st edition of Towards Blockchain-Based Collaborative Enterprise, TBCE 2019, and the 1st edition of smart data integration And Processing on Service based environments, stRAPS 2019, held in conjunction with the 17th international conference on Service-Oriented Computing, ICSOC 2019

15th International Workshop on Engineering Service-Oriented ...

引用

15th international Workshop on Engineering Service-Oriented Applications and Cloud Services, WESOACS 2019, the 4th international Workshop on Adaptive Service-Oriented and Cloud Applications, ASOCA 2019, the 4th international IoT systems Provisioning and Management for Context-Aware smart Cities, ISYCC 2019, the 1st edition of Towards Blockchain-Based Collaborative Enterprise, TBCE 2019, and the 1st edition of smart data integration And Processing on Service based environments, stRAPS 2019, held in conjunction with the 17th international conference on Service-Oriented Computing, ICSOC 2019

ISBN: (纸本)9783030459888

The proceedings contain 29 papers. The special focus in this conference is on Engineering Service-Oriented Applications and Cloud Services. The topics include: A Blockchain Based Solution for Securing data of IoT Devices;Toward GDPR Compliance in IoT systems;a Reconfigurable Microservice-Based Migration Technique for IoT systems;towards the Creation of Be In/Be Out Model for smart City with the Use of Internet of Things Concepts;ontology for smart Viticulture: Integrating Inference Rules Based on Sensor data;model-Driven Engineering for Multi-party Interactions on a Blockchain – An Example;smart Contract Locator (SCL) and smart Contract Description Language (SCDL);measuring the Quality of Life in "La Condesa": Activating Mexico City Neighbourhood Economy While Maximising Well-Being;adaptive Agent-Based Architecture for Health data Integration;constructing a Secured, Reactive and Scalable data Platform for a Better Exploitation of Rich data Assets in the Tourism Industry;freshening the Air in Microservices: Resolving Architectural Smells via Refactoring;towards Multi-level Trust-Driven data Integration in Multi-cloud Environments;user-Oriented Description of Emerging Services in Ambient systems;a Web-Component-Based Cross-Platform Mobile Application Development Environment for Ordinary Users;OSPAci: Online Sentiment-Preference Analysis of User Reviews for Continues App Improvement;ADS4all: Democratizing Authenticated data structures;ProMoEE - A Lightweight Web Editor Supporting study Research on Process Models;The EDMM Modeling and Transformation System;blockMeds: A Blockchain-Based Online Prescription System with Privacy Protection;janus: A Tool to Modernize Legacy Applications to Containers;a Programming Framework for People as a Service;Specifying Web Interfaces for Command-Line Applications Based on OpenAPI;microservices Management on Cloud/Edge Environments.

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Target Classification in Radar Using Deep Learning 1

Target Classification in Radar Using Deep Learning

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1st IEEE international conference on smart and Sustainable Developments in Electrical Engineering, SSDEE 2025

作者： Sahu, Suchitra Abhiram, Perumulla Menghal, P.M. Birla Institute of Technology and Science Pilani - Hyderabad Campus Department of Electrical and Electronics Engineering Hyderabad India Military College of Electronics and Mechanical Engineering Secunderabad Faculty of Electronics Telangana Hyderabad India

ISBN: (纸本)9798331542108

In radar systems, precise target classification is essential for effective situational awareness. We developed a Convolutional Neural Network (CNN)-based approach to classify radar targets using their signal characteristics automatically. Compared to traditional methods, our solution demonstrates higher accuracy, robustness, and computational efficiency while addressing challenges like data labelling, interpretability, and signal variability. The results highlight the potential of deep learning to transform radar systems, enhancing applications in military surveillance and autonomous navigation. This work marks a significant step toward seamlessly integrating deep learning with radar technologies. © 2025 IEEE.

关键词： Surveillance radar

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A Fuzzy Logic Based (FLB) Hybrid Level of Approach in the Evaluation of Security Impact in Healthcare Type of Web Applications for Secure Informations 1

A Fuzzy Logic Based (FLB) Hybrid Level of Approach in the Ev...

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1st international conference on Innovative Sustainable Technologies for Energy, Mechatronics and smart systems, IstEMS 2024

作者： Arthi, A. Sahu, Dillip Narayan Karpagam College of Engineering Computer Science and Technology Coimbatore641032 India Gangadhar Meher University Department of Mca Odisha India

ISBN: (纸本)9798350384246

The persistent breaches of private medical information in the healthcare sector provide a significant obstacle for companies operating in the sector. In addition to protecting sensitive data, building a strong and secure data safety structure for healthcare web apps is essential for improving the standing and earnings of healthcare organizations. Adopting a multi-criteria decision process becomes a crucial step towards achieving this goal. This study provides developers with a methodical road map by contributing both theoretical frameworks and real-world implementations. The research intends to assist practitioners in developing safe and efficient information security measures inside web applications by determining and putting first critical aspects, guaranteeing a noticeable influence on the entire cybersecurity environment. © 2024 IEEE.

关键词： Sensitive data

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The Role of smart Logistics Toward Sustainable Logistics: A Review 1

The Role of Smart Logistics Toward Sustainable Logistics: A ...

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1st international conference on Logistics, ICL 2024

作者： Al Rejal, Hussein Mohammed Abu Al-Ormuza, Tareq Khaled Sharif, Kamal Imran Mohd Udin, Zulkifli Mohamed Hassan, Mohamad Ghozali Kafi, Md. Abdul Mohammed, Fathey Abu, Noor Hidayah School of Technology Management and Logistics Universiti Utara Malaysia Kedah Malaysia School of Computing Universiti Utara Malaysia Kedah Malaysia

ISBN: (纸本)9798331518844

Given the significance of human-centeredness, resilience, and sustainability, smart logistics systems have pushed sustainable logistics. This paper discussed briefly and explained the smart logistic systems for sustainable logistics. The primary of sustainable smart logistics is the technology base and alternatives, data protection and risk tolerance, managerial support, customer influences, and social support. However, much research investigated the effects of disruptive technologies on smart logistics operations in Industry 4.0. The methodology used in this article included a literature review, case study, data analyses, and research paper from the year 2019 to 2022. The research result of this article is smart logistics systems toward logistics sustainability have striven to develop intelligent and efficient supply chains based on cooperative and adaptable networks and networked organizations. This study contributes to the growth of a body of literature that analyses the unexplored implications of Logistics 4.0 on the sustainability performance of enterprises. In addition to being the first of its kind for logistics performance evaluation, the proposed framework incorporates crucial problems of corporate sustainability and technological adaption. © 2024 IEEE.

关键词： industrial revolution 4.0 Review smart Logistics Sustainable Logistics

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Comprehensive Review of smart Healthcare Recommendation systems Using Deep Learning 1

Comprehensive Review of Smart Healthcare Recommendation Syst...

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1st international conference on Innovative and Intelligent Information Technologies, IC3IT 2024

作者： Bouslah, Soumeya Benharzallah, Saber Hamouma, Moumen Kahloul, Laid University of Batna 2 Lamie Laboratory Departement of Computer Science Batna Algeria University of Biskra Linfi Laboratory Departement of Computer Science Biskra Algeria

ISBN: (纸本)9798331541064

As smart cities advance and increasingly use data from Internet of Things (IoT) sources to enhance urban environments, smart healthcare has become a key focus area. This paper investigates the integration of Deep Learning (DL) techniques in smart Healthcare Recommendation systems (SHRSs), presenting a foundational taxonomy, that categorizes these systems into four distinct types: Content-Based Filtering (CBF), Collaborative Filtering (CF), Context-Aware Filtering (CAF), and Hybrid Filtering (HF). These approaches, implemented through DL, enable advanced data processing, pattern recognition, and personalization. Additionally, the paper introduces a new classification based on the nature of intervention, dividing SHRSs into preventive, diagnostic, treatment-focused, and hybrid categories. This dual classification fills a critical gap in the comparative analysis of DL-driven healthcare, which demonstrates the efficacy of these systems in providing personalized health advice, leveraging real-time sensor data and optimizing the handling of complex health datasets. Furthermore, this research outlines future work and potential directions for advancing SHRSs, offering insights into the application and impact of sophisticated recommendation techniques within the context of urban health management. © 2024 IEEE.

关键词： Electronic health record

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Development of Cyberattacks Detection Framework of Power systems Using Optimal Features in smart Grids 1

Development of Cyberattacks Detection Framework of Power Sys...

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1st international conference on Advances in Computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Sornalakshmi, R.R. Sailaja, Petikam Ramakrishnan, Madhusudana Aadhithya, S.M.R. Shreeshanth, R. Vidiyalarasu, S. Saveetha School of Law SIMATS Chennai600077 India

ISBN: (纸本)9798331518578

smart grids (SG) are a kind of sophisticated cyber-physical system (CPS) that allows power systems to be operated and controlled remotely by integrating smart devices' communication capabilities into the grid. However, this integration reveals many shortcomings in conventional SCADA (supervisory control and data acquisition) structures, which could jeopardise security objectives and present a significant cyberthreat to the SG. Therefore, in order to prevent grid sabotage, early detection of cyberattacks is essential. Conventional techniques for identifying cyberattacks that take use of smart grid network characteristics include machine learning (ML) techniques. An effective and efficient security control method is put forth in this work to identify cyberattacks on the SG. To increase detection efficiency, irrelevant characteristics are eliminated using the correlation-based feature selection (CFS) method. Using the chosen best attributes, an instance-based learning (IBL) system distinguishes between typical and abnormal occurrences. Depends on a limited set of features extracted from power system measurements of SCADA, experimental findings demonstrate that the suggested method achieves a high detection rate. © 2025 IEEE.

关键词： Power grids

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Cloud Based Automated Control System Workshops and Rooms for Controlling Parameters 1

Cloud Based Automated Control System Workshops and Rooms for...

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1st international conference on Advances in Computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Kumar, Dinesh Kuntal, Ravinder Singh Deep, Pragya Chamoli, Anup Shekhar Singh, Pinki Mandal, Rishabh SRM Institute of Science & Technology Department of Computer Science and Engineering UP Ghaziabad India Nitte Meenakshi Institute of Technology Department of Mathematics Karnataka Bangalore India IIMT College of Engineering Department of AI & DS Greater Noida India Uttaranchal School of Computing Sciences Uttaranchal University Dehradun248001 India Accurate College of Engineering & Management Department of Computer Science and Engineering Greator Noida India Tula's Institute Department of Computer Science and Engineering UK Dehradun India

ISBN: (纸本)9798331518578

Across all industries, cloud-based automated control systems have completely changed how operational and environmental parameters are tracked and controlled. With cloud-integrated systems for real-time data collection, processing, and control, this concept presents intelligent, remote-controlled workshops and control rooms. To guarantee accurate control of crucial variables like temperature, humidity, pressure, and equipment efficiency, these systems make use of IoT devices, AI techniques, and big data analytics. Through sophisticated data perspectives, the cloud-based strategy enables predictive maintenance, improves flexibility, and lowers operating expenses. stakeholders may make prompt, data-driven choices from anywhere with remote access to control rooms. These seminars provide practical experience with cloud-controlled settings and function as training and development centres. The system's flexibility to meet a range of industrial demands is further enhanced by its compatibility with external tools and equipment. The design, deployment, and possible uses of cloud-based control systems are covered in this paper, with an emphasis on how they might promote operational excellence, energy efficiency, and smart manufacturing. © 2025 IEEE.

关键词： smart manufacturing

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smart Traffic Management Solution integrating Machine Learning and Internet of Things 1

Smart Traffic Management Solution integrating Machine Learni...

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1st international conference on Innovative Engineering sciences and Technological Research, ICIEstR 2024

作者： Batra, Isha Malik, Arun Sharma, Shamneesh Gochhait, Saikat Lovely Professional University School of Computer Science and Engineering Punjab India UpGrad Education Private Limited Delivery and Student Success-upGrad Campus Bangalore India Symbiosis International Deemed University Sidtm Maharashtra Pune India Samara State Medical University Nri Russia

ISBN: (纸本)9798350348637

Increase in population and trend for luxurious lifestyle has led to huge increase in the number of vehicles. This change has further fascinated the researchers to focus on smart transportation systems. Numerous applications are offered in smart transportation systems like smart parking, detecting event of accidents, smart lights, and many more. Detecting event of accidents and informing vehicles on the move is the most prominent area of research now days. Technologies such as Internet of Things (IoT), Machine Learning (ML) can help achieve goals of smart transportation system. In this research paper, a smart architecture for accident detection and information dissemination is proposed. Initially, the data is collected using IoT sensor. After data collection, decision making is made to find out whether accident has occurred or not and the associated congestion in network using ML techniques. This information is finally disbursed to the neighboring vehicles using a secure method for data communication. The vehicles after receiving this information will decide whether to continue moving or choose some alternate path for communication. This research work emphasizes results after applying ML method and evaluates the performance of method in terms of accuracy, precision, and recall. © 2024 IEEE.

关键词： Decision making

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A Design of Hybrid Model and Bayesian Neural Networks for smart Grid stability Prediction 1

A Design of Hybrid Model and Bayesian Neural Networks for Sm...

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1st IEEE international conference on smart and Sustainable Developments in Electrical Engineering, SSDEE 2025

作者： Hole, Shreyas Rajendra Kolluru, Vinothkumar Salotagi, Shreekant Challagundla, Yagnesh Mungara, Sudeep Sriramkumar, R. Dayananda Sagar University AI & ML Bangalore India Stevens Institute of Technology Department of Data Science HobokenNJ07030 United States Dayananda Sagar University Computer Science & Engineering Bagalore India Herbert Wertheim College of Engineering University of Florida Department of Computer Science GainesvilleFL32611 United States Stevens Institute of Technology Masters in Information Systems HobokenNJ07030 United States

ISBN: (纸本)9798331542108

The latest evolution in power systems, is 'smart grids'that offers real time monitoring, control features as well as effective management of renewable energy sources. Nonetheless, with the increase in the system complexity, the chances for insta- bility also increase and thus it is crucial to forecast the stability accurately for the safety of the grid. Weather it is in terms of Gradient Boosting Tree Algorithm or any other traditional algorithm, these machine learning models are successful in achieving the objectives of the task but they lack a forthcoming metric to assist in high stake situations, uncertainty quantification. The paper is devoted to the analysis of the reliability of smart grid systems based on Bayesian Neural Networks (BNN) taking into account both the reliability of predictive modeling and forecasting uncertainty. The smart grid stability augmented dataset was used to benchmark the efficacy of using BNNs or BNNs combined hybrid models, one with XGBoost, for feature extraction and the other with LightGBM. The present study shows that the BNN used with the above mentioned smart grid system achieved an accuracy of 92% with precision of 0.89, recall of 0.91 and F1 of 0.90. These metrics are appending with the help of the additional features of BNN where they are capable of addressing the key questions around uncertainty and thus the metrics confirming the predictions are quite strong in terms of real-time grid stability evaluation. This article encapsulates a detailed study of BNN architecture, the role of uncertainty in machine learning models and the natural application of such models in the design of dependable and fault-tolerant smart grids. © 2025 IEEE.

关键词： smart power grids

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