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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Ahmed Adil Nafea Mustafa Maad Hamdi Baraa saad Abdulhakeem Ahmed Thair Shakir Mustafa S. Ibrahim Alsumaidaie Ali Muwafaq Shaban dept. Artificail inteligence College of Computer Science and Information Technology University of Anbar Ramadi Iraq dept. of Computer Science College of Computer Science and IT University of Anbar Ramadi Iraq dept. of Information System College of Computer Science and IT University of Anbar Ramadi Iraq dept. of Biophysics College of Applied Sciences University of Anbar Ramadi Iraq dept. Information System College of Computer Science and Information Technology University of Anbar Ramadi Iraq

ISBN: (数字)9798350374131

ISBN: (纸本)9798350374148

The Distributed Denial-of-Service (DDoS) attacks are one of the most critical threats to the stability and security of the Internet. With the increasing number of devices connected to the Internet, the frequency and severity of DDoS attacks are also increasing. To mitigate the impact of DDoS attacks, intelligent detection systems are becoming increasingly important. This paper reviews the recent literature on intelligent techniques, including machine learning (ML), Deep Learning (DL), and artificial intelligence (AI), for detecting DDoS attacks. We will provide an overview of the existing research in the field and analyse the trends in using time series data analysis for DDoS attack detection. A taxonomy and conceptual framework for DDoS mitigation are presented. This study highlights the use of several intelligent techniques for detecting DDoS attacks and evaluates the performance utilizing real datasets and also discusses future research directions in this field.

关键词： Data analysis Reviews Time series analysis Taxonomy Reinforcement learning Denial-of-service attack Stability analysis

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Data driven Approaches for Demand Forecasting in Supply Chain for Business Decisions

Data driven Approaches for Demand Forecasting in Supply Chai...

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International Conference on Advancements in Computational sciences (ICACS)

作者： Salman Javed Rizwan Akhtar Dept. IT and Computer Science Pak-Austria Fachhochschule Institute of Applied Sciences and Technology Haripur Pakistan

Demand forecasting is viewed as a complex challenge for industries as it relies heavily on various factors. The Supply Chain Management (SCM) integrates processes vital for corporate environments across industries. This paper presents a comprehensive data-driven framework for sales demand forecasting in SCM aiming to enhance accuracy of forecasts. Using Rossmann Stores data case study the sales are predicted up to 6 weeks ahead with a comparative analysis conducted for long-term predictions. Multi-dimensional data complexity is addressed by identifying relevant features and selecting appropriate algorithms. The proposed framework utilizes four data-driven machine learning algorithms: Extreme Learning Machine (ELM), Decision Tree (DT), Random Forests (RF), and Extreme Gradient Boosting (XGBoost) aiming to construct a high-precision sales demand forecasting model. The experimental validation using Rossmann Stores data demonstrates the effectiveness of the proposed framework, highlighting its significance in improving the accuracy of forecasts in SCM and enhancing decision-making processes.

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An Innovative Tool for Uploading/Scraping Large Image Datasets on Social Networks

arXiv

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arXiv 2023年

作者： Arceri, Nicolò Fabio Giudice, Oliver Battiato, Sebastiano Dept. of Math and Computer Science University of Catania Catania Italy

Nowadays, people can retrieve and share digital information in an increasingly easy and fast fashion through the well-known digital platforms, including sensitive data, inappropriate or illegal content, and, in general, information that might serve as probative evidence in court. Consequently, to assess forensics issues, we need to figure out how to trace back to the posting chain of a digital evidence (e.g., a picture, an audio) throughout the involved platforms—this is what Digital (also Forensics) Ballistics basically deals with. With the entry of Machine Learning as a tool of the trade in many research areas, the need for vast amounts of data has been dramatically increasing over the last few years. However, collecting or simply find the "right" datasets that properly enables data-driven research studies can turn out to be not trivial in some cases, if not extremely challenging, especially when it comes with highly specialized tasks, such as creating datasets analyzed to detect the source media platform of a given digital media. In this paper we propose an automated approach by means of a digital tool that we created on purpose. The tool is capable of automatically uploading an entire image dataset to the desired digital platform and then downloading all the uploaded pictures, thus shortening the overall time required to output the final dataset to be analyzed. © 2023, CC BY-NC-SA.

关键词： Digital forensics

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IoT-Enabled Smart Manhole Management System for Real-time Status, Water Level, and Gas Detection

IoT-Enabled Smart Manhole Management System for Real-time St...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Imran, Md Asif Masfequier Rahman Swapno, S.M. Chhabra, Gunjan Kaushik, Keshav Anzum Mahi, Md. Arif Islam, Md Babul Haque, Rezaul Bangladesh University of Business and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh Graphic Era Hill University Department of Cse Uttarakhand Dehradun India Graphic Era Deemed to Be University Uttarakhand Dehradun India School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun India Modeling Electronic and System Engineering Unical Dept. of Computer Rende Italy Applied Bahari Big Data Research House Dhaka Bangladesh Modeling Electronic and System Engineering Unical Department of Computer Rende Italy

ISBN: (纸本)9798350375237

Regular monitoring of the maintenance manhole system is imperative to uphold the city's health and cleanliness standards. Our study offers an intelligent manhole that monitors the manhole's condition, water level, and gases. We tested the status, gas, and water levels of our revolutionary, innovative manhole in several places. Our system's Blink app position tracking of our smart manhole is critical. This capability improves monitoring efficiency and speeds up issue resolution. Our work revolutionizes municipal utility maintenance and safety by optimizing manhole performance. Integrating a Node MCU, GPS module, gas sensor, ultrasonic sensor, and Android Blynk software creates a complete real-time monitoring and data-collecting system. Combining these technologies, our Manhole Monitoring System improves safety and streamlines maintenance, making municipal infrastructure more robust and efficient. This paper highlights the promising features of IoT and shows how it may be used to improve the smart manhole. In the future, this creative method may result in a more durable and better intelligent city. © 2024 IEEE.

关键词： Maintenance

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Parhit: An Innovative Full-Stack Donation Platform with Dynamic-Routing Approach in ReactJS 7

Parhit: An Innovative Full-Stack Donation Platform with Dyna...

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7th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2023

作者： Joshi, Shivangi Pushpad, Vishal Dutt, Nabya Pandey, Yaghik Choudhury, Tanupriya Kotecha, Ketan School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun248007 India Cse Dept. Pune India Symbiosis Centre for Applied Artificial Intelligence Pune411045 India

ISBN: (纸本)9798350342154

In today's world, people have increasingly diverse requirements and interests that span a wide range of products. The pressing global issues now extend beyond hunger to encompass the various needs of individuals, including clothing, footwear, bags, and everyday household items. By developing a web-based online system using *** with a dynamic routing approach, we can provide an easy and efficient way for people to donate their used belongings to charitable organizations and nonprofits. This technology-driven system will simplify the donation process by leveraging the power of the internet. It will expedite the collection and distribution of old items so that they reach the appropriate organizations or individuals in a timely manner. Our primary focus is on implementing a dynamic-routing approach through *** framework which allows users to seamlessly navigate through different sections of the website. Additionally, this paper delves into incorporating features like user authentication and secure data storage management using Firebase to ensure confidentiality and efficiency when handling sensitive information. © 2023 IEEE.

关键词： Websites

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A Quick Heuristic Algorithm for Enforcing the Liveness of S3PR Petri Nets 22

A Quick Heuristic Algorithm for Enforcing the Liveness of S3...

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22nd International Symposium INFOTEH-JAHORINA, INFOTEH 2023

作者： Karatkevich, Andrei Grobelna, Iwona Agh University of Science and Technology Dept. of Applied Computer Science Kraków Poland University of Zielona Góra Autom. Control Electronics and Electr. Eng. Zielona Góra Poland

ISBN: (纸本)9781665475464

The optimal deadlock avoiding, deadlock recovery, as well as deadlock detection in Petri nets are the NP-hard problems. For this reason, heuristic algorithms for finding the approximate solutions of such problems are of a great interest. In the paper a heuristic method of deadlock recovery for the S3PR nets, which are often used for modelling of manufacturing systems, is proposed. The method is based on the idea of resetting the process instances in the S3PR nets and a heuristic for breaking the cycles in the wait-for graphs. The method does not require any time consuming operations such as construction of the reachability graph, explicit detecting of all reachable deadlocks or all elementary cycles in a wait-for graph, hence it is quick. The simple recovery transitions are added to the net providing recovery from both global and local deadlocks. © 2023 IEEE.

关键词： Petri nets

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Design of a Gamified Training System for Human-Robot Cooperation

Design of a Gamified Training System for Human-Robot Coopera...

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2022 International Conference on Electrical, computer, Communications and Mechatronics Engineering, ICECCME 2022

作者： Ateş, Gizem Kyrkjebø, Erik Dept. of Computer Science Electrical Eng. and Mathematical Sciences Western Norway University of Applied Sciences Førde Norway

ISBN: (数字)9781665470957

ISBN: (纸本)9781665470957

Human-Robot Cooperation (HRC) is a field which focuses on employing the best skills of both the robot and the human working together to achieve a common or shared task more efficiently. In most cases, both the human and the robot should know the status of each other and interchange data accordingly. There are several successfully researched HRC systems in the literature proposing solutions to various industrial problems yet few of them are used actively in real-world tasks. One of the major reasons for this gap is that the developed HRC systems do not offer an effective training procedure. The availability of a versatile training setup to get the optimal efficiency is important in addition to evaluating the developed HRC system's usability. Recent studies show that serious games offer effective training outcomes in various sectors such as the military, disaster drills, aviation, health etc. This paper presents an open-source gamified modular training design for HRC applications. It shows how the HRC system can be trained using serious games, what game elements can be utilized and how the learning curve of the user can be measured to evaluate the usability and efficiency. The proposed design is demonstrated through a real-world Cooperative Lifting (co-lift) scenario. The main motivation is to constitute a baseline for effective training of the HRC systems so that the gap between research and successful innovation in the HRC field becomes more narrow. © 2022 IEEE.

关键词： Serious games

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Human Body Action Recognition based on 3D Feature Fusion over RGB Scenes

Human Body Action Recognition based on 3D Feature Fusion ove...

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International Conference on Information Technology (ICIT)

作者： Heba Nsour Zaid Mustafa Maria Al-Sendah Dept. of Computer Science Al-Balqa Applied University Albalqa Jordan Dept. of Computer Information Systems Al-Balqa Applied University Albalqa Jordan

Human body action recognition (HBAR) is an important area of research in machine learning and image processing due to its vast range of applications. Similarly, estimating various components of human anatomy from RGB scenes are essential to human action tracking. The present study involves the implementation of human key body point detection for pose estimation. During abstracted silhouettes and vital human points, the proposed system has extracted two main features, such as 3D features and distance features. Once the relevant attributes have been incorporated, executing precise tasks and strategies is critical to achieving maximum features. Therefore, we used the t- SNE-based data refinement technique for optimal feature selection. Finally, for training, a classification model random forest is utilized. The presented system is verified on a recognized benchmark dataset, i.e., the UCF sports database. The experimental settings have revealed that our proposed system has attained better performance and outperformed other present state-of-the-art methods regarding mean recognition rate.

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Park-Space: A Parking Space Indicator 3

Park-Space: A Parking Space Indicator

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Khanduja, Dhairya Mittal, Janvi Choudhury, Tanupriya Kotecha, Ketan Singh, Manhar Sinha, Suryansh School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun248007 India Cse Dept. Uttarakhand Dehradun248007 India Cse Dept. Maharashtra Pune412115 India Symbiosis Center for Applied Artificial Intelligence Pune412115 India

ISBN: (纸本)9798350343274

The Parking Spot Indicator Application is a software program that aims to simplify the process of finding parking spaces in busy lots for drivers. Utilizing sensors installed in each spot, it monitors their occupancy status in real-time and presents this information through an easy-to-use interface. This application offers drivers, lot operators, and the environment several benefits. Drivers can save time and reduce stress by avoiding the hassle of searching for a spot, thanks to the real-time updates provided by the application. Operators can optimize resources, reduce congestion, and increase revenue by effectively managing their lots. Additionally, this application helps minimize traffic congestion and emissions by reducing circling time spent looking for parking spots. It is designed to be scalable and adaptable to various types of lots, including outdoor areas, multi-level structures, and underground facilities. The user-friendly interface provides a simple experience with a real-time map displaying available spots highlighted. © 2023 IEEE.

关键词： Application programs

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Secure Risk Management Approach for Enhancing the Performance of Cloud-based Cooperative Spectrum Sensing CRNs

Secure Risk Management Approach for Enhancing the Performanc...

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IEEE International Symposium on Network Computing and Applications

作者： Victor Balogun Xavier Paquette Oluwafemi A. Sarumi Dept. of Applied Computer Science University of Winnipeg Winnipeg MB Canada Institute of Computer Science Heinrich-Heine-University Duesseldorf Duesseldorf Germany

ISBN: (数字)9798331510183

ISBN: (纸本)9798331510190

The increasing demand for radio spectrum, spurred by an upsurge in interconnected devices, necessitates innovative management solutions. Cognitive Radio Networks (CRN) offer a promising approach to dynamically allocate spectrum resources. This study examines the Cooperative Spectrum Sensing (CCSS) architecture, leveraging cloud computing and big data analytics, to enhance CRN efficacy. We present a risk analysis identifying key vulnerabilities, explore regulatory considerations within the Canadian spectrum landscape, and propose an augmented architecture incorporating preventative and defensive strategies. We also discuss challenges encountered in the implementation phase and suggest future research trajectories for cloud-assisted CCSS systems.

关键词： Cloud computing Cooperative communication computer architecture Big Data Trajectory Sensors Risk management Security Cognitive radio Radio spectrum management

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