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An Open Source Trusted Edge Architecture of Federated Dataspaces for the Food Supply Chain 1st

An Open Source Trusted Edge Architecture of Federated Datas...

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1st international Workshop on the Design, Verification, and Validation of IoT Systems, GRAAL4IoT 2024 and 1st international Workshop on IoT Standards, STAND4IoT 2024, held in conjunction with 7th IFIP WG 5.5 international Cross-Domain conference on Internet of things, IFIPIoT 2024

作者： Tessmer, Alexander Mast, Jannis Loer, Philipp Brunner, Matthias Lippert, Felix Aschenbruck, Nils Osnabrueck University Institute of Computer Science Osnabrueck Germany Lippert Inspection GmbH Sinzig am Rhein Germany tsenso GmbH Stuttgart Germany

ISBN: (纸本)9783031820649

Currently, quality assurance in the food supply chain is predominantly conducted manually with traditional destructive methods. Best before date forecasts depend mainly on time of arrival without including weather or cold chain. Measured data within the food supply chain is not digitized and can therefore not be aggregated for analysis. We propose a new architecture for improving sustainability of the food supply by combining innovative measurement techniques with a trusted cloud edge infrastructure. Forecasts based on digital twins and a shared federated data space for freshness data serve to optimize supply chain processes and reduce the loss of fresh produce along the supply chain. In this paper, we present a suitable system architecture with independent data spaces and flexible implementations for trusted local deployments. © IFIP international Federation for Information Processing 2025.

关键词： Federated Dataspaces Food Supply Chain Trusted Edge

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Crime Forecasting : A theoretical Approach 7

Crime Forecasting : A Theoretical Approach

引用

7th IEEE international conference on Recent advances and Innovations in Engineering, ICRAIE 2022

作者： Anuvarshini, S.R. Nidhi, Deeksha Deeksha Sree, C. Krishna Sowjanya, K. CMR Institute of Technology Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665489102

Crime and its increase are detrimental to any society. High crime rates can affect the functioning of a city and its economy. the prediction and future forecasting of crime can help law enforcement officers curb crime in a city. there is a need for a better prediction system which intimates law enforcement regarding hot spots of crime in the city. Previous research shows a lower accuracy in crime prediction and forecasting using machine learning. therefore, this study includes the application of different Machine Learning models like, Decision Tree, Extra Tree Classifier, K-Nearest Neighbor, AdaBoost, Random Forest, Light Gradient Boosting and Gradient Boosting algorithms and time series analysis by Long Short Term Memory and Convolutional Neural Network, Auto Regressive Integrated Moving Average and Seasonal ARIMA to suit our *** performance of a fully convolutional neural network showed the most promising results. Moreover, identification of districts which have the most contribution to the total crime is also determined. this study is useful in assisting law enforcement in identification of districts with higher crime and future patterns of crime in the city of Boston. © 2022 IEEE.

关键词： Crime

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Sector-wise expert input selection for electricity load forecasting 7

Sector-wise expert input selection for electricity load fore...

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7th IEEE international conference on Recent advances and Innovations in Engineering, ICRAIE 2022

作者： Sharma, Megha Mittal, Namita Mishra, Anukram Gupta, Arun Malaviya National Institute of Technology Department of Computer Science and Engineering Jaipur India Genus Power Infrastructures Limited Jaipur India

ISBN: (纸本)9781665489102

In Smart Grid, forecasting power demand plays a vital role in demand side management. Forecasting medium-term load is important for power system planning. People depend on electricity for livelihood in the residential sector and for good productivity in the commercial, industrial and agricultural sectors. the sector-wise importance of characteristics for accurate load forecasting has been identified in this paper. First, we analyze the power consumption data in various sectors with different parameters, then various parameters (weather data and seasonal data) are identified that affect the power consumption pattern of the region, finally, a machine learning model is used to identify the sector-wise feature importance for load prediction. the Random Forest Regressor outperforms Multiple Linear Regression for load prediction. © 2022 IEEE.

关键词： Electric power utilization

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Plant Disease Classification Using VGG-19 Based Faster-RCNN 7th

Plant Disease Classification Using VGG-19 Based Faster-RCNN

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Proceedings of the 7th international conference on advances in Computing and Data sciences, ICACDS 2023

作者： Nawaz, Marriam Nazir, Tahira Khan, Muhammad Attique Rajinikanth, Venkatesan Kadry, Seifedine Department of Software Engineering University of Engineering and Technology Taxila Taxila47050 Pakistan Faculty of Computing Department of Computer Science Riphah International University Gulberg Green Campus Islamabad Pakistan Department of Computer Science HITEC University Taxila Pakistan Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering SIMATS Chennai602105 India Department of Applied Data Science Noroff University College Kristiansand4612 Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

ISBN: (纸本)9783031379390

Early plant disease diagnosis can help farmers avoid spending money on costly crop pesticides and aid them to boost food production. Scientists have put a lot of effort to classify plant diseases, but it is difficult to quickly locate and identify different crop abnormalities due to the high degree of resemblance between the normal and damaged parts of plant leaves. Additionally, the procedure of detecting plant diseases has been made more challenging due to the extensive color, size, shape, and intensity variations in the background and foreground of the plant images. To address the existing difficulties, we have introduced an effective deep learning (DL) based system called Faster-RCNN o recognize and classify various types of plant diseases. the suggested method consists of 3 basic steps. In order to identify the area of interest in investigated samples, we first create annotations for them which are later used for Faster-RCNN training. the Faster-RCNN model employs the VGG-19 network to extract the relevant keypoints from the given images which are later passed to the regressor and classification units to identify and categorize the various crop diseases using the estimated features. We evaluated our method on a widely used standard plant sample repository called the PlantVillage database, and the findings show that our approach is reliable for classifying plant diseases under a variety of image-capturing scenarios. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Crops

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Innovative advances: Crafting an Intelligent Controller for Home Energy Management Systems with Seamless Integration of Renewable Sources 7

Innovative Advances: Crafting an Intelligent Controller for ...

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7th international conference on Contemporary Computing and Informatics, IC3I 2024

作者： Varasree, B. Reddy, N.V. Uma Yadav, Suman Avdhesh Nijhawan, Ginni Garg, Monica Sirisha, N. Institute of Aeronautical Engineering Department of Information Technology Telangana Hyderabad India New Horizon College of Engineering Department of Artificial Intelligence & Machine Learning Bangalore India Iilm University Department of Cse Uttar Pradesh Greater Noida India Lovely Professional University Phagwara India Lloyd Law College Uttar Pradesh Greater Noida India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350350067

this research proposes an improved energy administration and optimization technique that utilizes fuzzy logic algorithms and incorporates many inputs and outputs (I/O) along with a cost-effective method. this technology is used in the administration of hybrid electric power sources, which include PV cell, turbines (wind), and grid-supportive devices. the novelty of this research lies in the precedence of solar PV, wind turbine, and storage system energy sources above the grid network. the grid network is only used in case of bad weather. this system can supply an average household with up to 4,000 Wh of energy per day. In addition, by using an electrolyzing system, surplus unconventional energy produced during good weather conditions is used to produce hydrogen, which is well-suited for household cooking and heating purposes. Developing an intelligent economic strategy by using fuzzy technology to bolster improved energy management and optimization methods. the fuzzy logic software at the heart of this system has been written on the Arduino 2560 mega microcontroller. It is responsible for allocating events and considering all possible circumstances. this system has been developed using a flowchart that facilitates hybrid system management. In order to maintain uninterrupted housing, it is advisable to use strategies such as methodologies, a parametric search, and simulations to precisely characterize the system components that provide assistance to the power grid. the effectiveness of the recommended method is confirmed by the display of output directives generated by the electronic switches. © 2024 IEEE.

关键词： Controllers Energy Management Systems Energy Optimization Renewable Energy Integration Sustainable Living

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TechSSN1@LT-EDI: Depression Detection and Classification using BERT Model for Social Media Texts 3

TechSSN1@LT-EDI: Depression Detection and Classification usi...

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3rd Workshop on Language technology for Equality, Diversity and Inclusion, LTEDI 2023

作者： Yenumulapalli, Venkatasai Ojus Vijai Aravindh, R. Rajalakshmi, S. Angel Deborah, S. Department of Computer Science and Engineering Sri Sivasubramaniya Nadar College of Engineering Tamil Nadu Chennai603110 India

ISBN: (纸本)9789544520847

Depression is a severe mental health disorder characterized by persistent feelings of sadness and anxiety, a decline in cognitive functioning resulting in drastic changes in a human’s psychological and physical well-being. However, depression is curable completely when treated at a suitable time and treatment resulting in the rejuvenation of an individual. the objective of this paper is to devise a technique for detecting signs of depression from English social media comments as well as classifying them based on their intensity into severe, moderate, and not depressed categories. the paper illustrates three approaches that are developed when working toward the problem. Of these approaches, the BERT model proved to be the most suitable model with an F1 macro score of 0.407, which gave us the 11th rank overall. © 2023 LTEDI 2023 - 3rd Workshop on Language technology for Equality, Diversity and Inclusion, associated with the 14th international conference on Recent advances in Natural Language Processing, RANLP 2023 - Proceedings. All rights reserved.

关键词： Social networking (online)

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Morphology-Based Machine Learning Mechanism for Unsupervised Framework Prediction Using Statistical Segmentation on Blood Cancer 5th

Morphology-Based Machine Learning Mechanism for Unsupervised...

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5th international conference on advances in Information Communication technology and Computing, AICTC 2024

作者： Ishavarbhai, Moravani Poojaben Sharma, Ajay Kumar Patel, Mayank Rathore, Narendra Singh Department of Computer Science Engineering GITS Udaipur India GITS Udaipur India

ISBN: (纸本)9789819761050

Leukaemia is a disease that is related to cancerous cells. It is also lethal, and people of all age groups can be affected by it. WBCs especially affect this, and they are followed by a growth in the amount of lymphocytes that are still not mature and they can affect the blood or bone marrow. Hence, it is mandatory that periodic and proper cancer diagnosis should be carried out so that it can be detected at an early stage. At present, the process of detecting this deadly disease is manual. the blood samples collected using microscopic pictures which is highly sluggish and consumes a lot of time. Hence, an attempt has been done to use a new machine learning algorithm using a statistical segmentation. the proposed model can be utilized to create a suitable computer-aided diagnosis for leukaemia malignancy. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Comprehensive Study of Cyber Security in AI Based Smart Grid 1

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Proceedings of the 7th international conference on advances in Computing and Data sciences, ICACDS 2023

作者： Sanghavi, Priyansh Solanki, Riya Parmar, Viral Shah, Kaushal School of Technology Pandit Deendayal Energy University Gujarat Gandhinagar382007 India

ISBN: (数字)9783031379406

ISBN: (纸本)9783031379390

One of the most beneficial purposes of artificial intelligence is the smart grid. the whole globe is rapidly migrating from traditional grid systems to smart grids driven by AI. One of the most difficult jobs is protecting the smart grid from cyber assaults. Any cyber-attack may compromise the smart grid’s confidentiality, integrity and availability. Significant research is being performed in industry, government, and academia to improve smart grid security in order to secure the smart grid from intruders. In this paper, we explore the history of cyber-attacks on smart grids throughout the globe, various cyber-attacks, and countermeasures in smart grid cyber-security. this research examined the difficulties and potential solutions of such AI-based smart grids. We highlight cyberattack types and conduct an in-depth study of the smart grid’s cyber security. Emphasis is placed on the discussion & investigations of security breaches, assault countermeasures, and security requirements. We aim to develop a comprehensive understanding of cyber-security threats and countermeasures in the context of smart grid technologies, as well as a road strategy for further cyber-security studies in this area. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Analysis of Routing in IOT 1

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Proceedings of the 7th international conference on advances in Computing and Data sciences, ICACDS 2023

作者： Shrivastava, Garima Kumar, Mahesh Infosys Bangaluru Bangalore India Computer Science and Engineering Jaypee University of Engineering and Technology A.B Road Guna473226 India

ISBN: (数字)9783031379406

ISBN: (纸本)9783031379390

Internet has grown exponentially in recent years. Now-a-days, everything that performs some function is supposed to be connected to the internet. Most of the devices in everyday life are not only small in size but also lack processing capabilities. this becomes a bottleneck in bringing these devices online. Internet of things often referred to as IOT is a concept that involves tools, technologies and methodologies to connect these devices to the internet. In order to achieve this objective, many standards and protocols are devised so far to ensure proper functionality of IOT networks. IOT networks consist of usually a very large number of devices with minimal computation and storage capabilities. Hence, IOT networks should be designed in such a way to support a high degree of scalability. Another key characteristic of IOT networks is wireless connectivity. Many technologies like Bluetooth, Wi-Fi, Zigbee, etc. are widely used to establish connectivity among these devices. the fact that IOT consist of so many devices lead to generation of a huge amount of data. Routing this data with limited capacities becomes one of the core issues of IOT. One of the most widely used routing protocol for IOT is RPL. To gain a better understanding of the applicability and operationality of routing protocol like RPL, this paper suggests analysis through simulation using Cooja simulator over Contiki operating system. the simulation result helps in improving decision making process regarding the choice of right version of the routing protocol according to given network environment. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Routing protocols

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A Systematic Survey on Various Distributed Denial of Service (DDoS) Attacks in Wireless Sensor Networks (WSN) 7

A Systematic Survey on Various Distributed Denial of Service...

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7th IEEE international conference on Recent advances and Innovations in Engineering, ICRAIE 2022

作者： Keerthika, M. Shanmugapriya, D. Avinashilingam Institute for Home Science and Higher Education for Women Department of Computer Science Coimbatore641043 India Avinashilingam Institute for Home Science and Higher Education for Women Department of Information Technology Coimbatore641043 India

ISBN: (纸本)9781665489102

Abstract: In this digital era, Wireless Sensor Networks (WSN) are the most popular technological trends and are already a leading technology used for mission carried out in remote areas and also for future applications. Because WSN are broadcast, the wireless air interface is open and available to all types of users, posing several unique security difficulties for researchers. A Distributed Denial of Service attack (DDoS) is a network attack in which a victim involving many machines is dispersed across multiple and independent networks. the stream of incoming messages effectively pushes the target system to shut down, denying authorized users access to the system. WSN nodes are vulnerable to various attacks, which includes Black hole, Denial of service (DoS), and DDoS attacks, all of which are discussed in this paper. A DDoS attack is a significant security threat in today's communications networks. New methodologies for determining system vulnerabilities have been implemented as new vulnerabilities have been discovered. Much research hasn't been done on DDoS in the WSN;a review of DDoS attacks is being conducted to demonstrate their impact on networks. DDoS attacks can have significant effects, especially for businesses that rely on the internet for their operations. the purpose of the study is to know about the various impact of DDoS attacks and this paper also elaborately discusses different types of DDoS attacks in WSN for other researchers to know attacks caused due to DDoS in different layers. © 2022 IEEE.

关键词： Denial-of-service attack

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