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Development of Additive Manufacturing Design Based on Machine Learning: A Biomedical Engineering Approach 1

Development of Additive Manufacturing Design Based on Machin...

1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Khan, Akbar Valivarthi, Dharma Teja Banik, Shampa Kakkad, Ashish Sujatha, B. Acharya, Deepak Nimra College of Engineering and Technology ECE Department Andhra Pradesh Vijayawada India Tek Leaders TX United States Dhaka Bangladesh Marwadi University Faculty of Physiotherapy Rajkot India Department of Computer Science and Engineering Tamilnadu Tiruchengode India The University of Alabama in Huntsville Department of computer science United States

ISBN: (纸本)9798331518578

Additive manufacturing (AM) is a versatile and complex manufacturing technique that is extensively employed in the production of personalized biopolymer-orientated items and sophisticated medical architecture. One of the most developed AM techniques is selective laser melting (SLM), which uses a laser as an energy resource to completely melt successive layers of granular resources. SLM produces titanium-oriented alloys that are highly biocompatible and have exceptionally excellent physical qualities, which have led to their widespread usage in biomedical uses. An artificial neural network-based optimizing approach was established to enhance the standard of Ti-6Al-4V SLM-based parts and assist manufacturing professionals in selecting optimum process settings. The approach relates 4 primary procedure settings to two target characteristics of the part. To improve the system's accuracy throughout neural network training, several techniques were employed to address undesirable issues. The denseness and surface hardness values of the system's R2 of 99 percent demonstrated its effectiveness. After that, a selection system was developed that lets users select the best process variables for manufactured goods whose attributes satisfy particular user requirements. The optimum part quality produced by tests using the ideal process variables suggested by the optimizing approach was almost the same as those specified by the user, with only 0.9 to 4.4 percent of mistakes at the high, significantly confirming the system's dependability. © 2025 IEEE.

关键词： Titanium alloys

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Electric Vehicles as Flexible Assets for Households and system Operators: Survey Results

Electric Vehicles as Flexible Assets for Households and Syst...

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International Conference on Smart systems and Technologies (SST)

作者： Nemanja Mišljenović Matej Žnidarec Goran Knežević Danijel Topić Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (数字)9798350386394

ISBN: (纸本)9798350386400

The transition towards electric vehicles (EVs) is a pivotal response to mitigate environmental concerns and reduce reliance on fossil fuels. This survey-based research aims to provide comprehensive insights into the stance of EV users in Croatia on using EVs as assets for flexibility in operation. Users are surveyed about their EV characteristics and habits, emphasizing charging preferences and possible subsidizing. Because of their larger degree of freedom in operation, family home users are further surveyed about their attitude toward load management, investments for additional sources of electricity and flexibility in operation, and advanced electricity billing models to ensure greater savings. Results show a very conservative stance of users toward actions that deteriorate their comfort level. Further on, users that dwell in a family house are prone to investments if it can generate additional savings and maintain or even increase their comfort level. This survey provided valuable findings for researchers in modeling EVs as a flexible asset in energy management studies, especially in the emerging field of energy communities.

关键词： Surveys Electricity Water heating Transportation Electric vehicles Washing machines Standards Load modeling Resistance heating Investment

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Optimizing the Shopping Experience Through Smart Trolleys with Integrated Deep Learning Techniques 4

Optimizing the Shopping Experience Through Smart Trolleys wi...

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4th International Conference on Sustainable Expert systems, ICSES 2024

作者： Akhila, T.S. Presskila, X. Arogya Kalyanakumar, P. Krishnan, R. Santhana Sahanapreethi, M. Sundaravadivel, P. Dhanalakshmi College of Engineering Department of Electronics and Communication Engineering Tambaram Chennai India Scad College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India Velammal College of Engineering and Technology Department of Information Technology Madurai India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Psna College of Engineering and Technology Department of Information Technology Dindigul India Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331540364

This research study presents an innovative smart shopping trolley system designed to enhance the retail shopping experience by integrating advanced deep learning techniques and sensor technology. The system begins with a secure membership card login process, enabling personalized customer interaction. Upon entry into the trolley system, products can be easily added and identified through a camera utilizing EfficientN et, a deep learning model trained on the store's inventory. The system incorporates an infrared motion sensor and a weight sensor to ensure accuracy in product registration, enhancing reliability and preventing errors. As the shopping process unfolds, a Transformer-based recommendation system analyzes real-time choices and shopping history to provide tailored product suggestions displayed on the trolley's LCD screen. To enhance security, an Autoencoder neural network monitors for anomalies in product identification and removal, alerting store personnel in case of discrepancies. Upon completing the shopping process, a QR code is generated for payment, streamlining the checkout experience. This smart shopping trolley system optimizes the shopping process, leveraging real-time data to improve efficiency, security, and overall customer satisfaction in the retail environment. The system aims to revolutionize traditional shopping methods by applying deep learning and IoT technologies and create a seamless, engaging consumer experience. © 2024 IEEE.

关键词： Customer satisfaction

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A Game Theory based Attacker Defender Model for IDS in Cloud Security 9

A Game Theory based Attacker Defender Model for IDS in Cloud...

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9th International Conference on Computing for Sustainable Global Development, INDIACom 2022

作者： Jain, Ashima Tripathi, Khushboo Jatain, Aman Chaudhary, Manju Amity University Department of Computer Science and Engineering Haryana Gurugram India Jaypee Institute of Information Technology Department of Computer Science and Engineering Noida India

ISBN: (纸本)9789380544441

Cloud security has become a serious challenge due to increasing number of attacks day-by-day. Intrusion Detection system (IDS) requires an efficient security model for improving security in the cloud. This paper proposes a game theory based model, named as Game Theory Cloud Security Deep Neural Network (GT-CSDNN) for security in cloud. The proposed model works with the Deep Neural Network (DNN) for classification of attack and normal data. The performance of the proposed model is evaluated with CICIDS-2018 dataset. The dataset is normalized and optimal points about normal and attack data are evaluated based on the Improved Whale Algorithm (IWA). The simulation results show that the proposed model exhibits improved performance as compared with existing techniques in terms of accuracy, precision, F-score, area under the curve, False Positive Rate (FPR) and detection rate. © 2022 Bharati Vidyapeeth, New Delhi.

关键词： Deep neural networks

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A Proposed Darknet Traffic Classification system Based on Max Voting Algorithms

A Proposed Darknet Traffic Classification System Based on M...

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International Conference on Cyber Security, Privacy and Networking, ICSPN 2022

作者： Almomani, Ammar Alauthman, Mohammad Alkasassbeh, Mouhammad Samara, Ghassan Liu, Ryan Wen School of Information Technology Skyline University College P.O. Box 1797 Sharjah United Arab Emirates IT-department- Al-Huson University College Al-Balqa Applied University P.O. Box 50 Irbid Jordan Department of Information Security Faculty of Information Technology University of Petra Amman Jordan Department of Computer Science Princess Sumaya University for Technology Amman11941 Jordan Computer Science Department Zarqa University Zarqa Jordan Hubei Key Laboratory of Inland Shipping Technology School of Navigation Wuhan University of Technology Wuhan China

ISBN: (纸本)9783031220173

The darknet refers to an anonymous address space on the Internet where people do not expect to interact with their computers so that it is considered malicious and provides them with a safe place where people can express themselves anonymously. Tools such as "tor" are also used in illegal activities such as disseminating child abuse, selling Drugs and Weapons, or distributing malicious software In addition to cyber-attacks. This proposed proposes a darknet traffic detection and classification system by using the Max Voting classifier Ensemble technique to produce the final predictions in a manner based on three algorithms: Random Forest and KNN and Gradient Boosting combined to get accurate results in case the client gets access to the internet using the darknet or not. As an expected contribution, the system will be got a higher Accuracy compared with the current systems, The proposed suggested working on a dataset of more than 100,465 records analyzed from the 2020 CIC-Dark collected from the Darknet. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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Efficient Road Status Monitoring in Vehicular Ad-Hoc Networks: An Optimization Framework

Efficient Road Status Monitoring in Vehicular Ad-Hoc Network...

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information technology (ACIT)

作者： Ghassan Samara Hussain Mohammed Turki Mahmoud Odeh Anas Al-Oraiqat Mohammad Aljaidi Raed Alazaidah Mohammad Rasmi Al-Mousa Ahmed BaniMustafa Mohammad Kanan Department of Computer Science Faculty of IT Zarqa University Zarqa Jordan Faculty of Information Technology Zarqa University Faculty of Information Technology Zarqa University Zarqa Jordan Computer Information Science Dept. Higher Colleges of Technology Abu Dhabi UAE Department of Industrial Engineering University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

The number of cars on the road in the world is currently increasing. With the widespread usage of vehicles, a slew of issues inevitably arises. The damage of the road can be annoying to the drivers, and congestion in traffic can waste both fuel and time. It's worth noting that one of the primary causes of car damage and aging is the state of the road surface. Driving comfort and vehicle controllability can be harmed by poor road conditions. As a result, the system of monitoring road status is crucial to reducing congestion in traffic, reducing accident rates, and protecting cars from hazardous road conditions. This paper proposes using a framework based on cloud-fog network architecture, The fog computing network architecture is a dispersed network that may process data at the fog layer to minimize cloud computing's computational load. In addition, using small and large threshold values ( ), Main Authority (MA) deals with the major conditions, and the Sub Authority (SA) deals with the minor conditions.

关键词： Cloud computing Roads Vehicular ad hoc networks Network architecture Throughput Delays Servers Automobiles Monitoring Edge computing

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Fusing AraBERT and Graph Neural Networks for Enhanced Arabic Text Classification 24

Fusing AraBERT and Graph Neural Networks for Enhanced Arabic...

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24th International Arab Conference on information technology, ACIT 2023

作者： Karajeh, Ola Al-Kabi, Mohammed N. Fox, Edward A. Virginia Tech Computer Science Department BlacksburgVA United States Al-Buraimi University College Information Technology Department Al Buraimi Oman

ISBN: (纸本)9798350384307

Text classification is a fundamental task in natural language processing, and has been widely studied for various languages. However, Arabic text classification is challenging due to the complexity of Arabic (i.e., rich morphological structure), high degree of ambiguity, and optional diacritics in the writing system. Large-scale pre-trained contextualized models (e.g., AraBERT) can successfully capture semantics in the local context. On the other hand, graph-based models could capture the global context by incorporating long-distance semantics. A text classification model combining the local and global context can enhance classifier performance for highly complex and ambiguous languages. In this work, we introduce the Arabic BERT Graph Convolutional Network (AraBERT-GCN), that can leverage the advantage of using large-scale pre-trained models alongside graph convolutional networks. Experimental results show that AraBERT-GCN outperforms the state-of-the-art (SOTA) on our Arabic text datasets. © 2023 IEEE.

关键词： AraBERT-GCN Arabic text classification Graph convolutional networks

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Cyberbullying Detection and Recognition with Type Determination Based on Machine Learning

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computers, Materials & Continua 2023年第6期75卷 5307-5319页

作者： Khalid M.O.Nahar Mohammad Alauthman Saud Yonbawi Ammar Almomani Computer Science Department Yarmouk UniversityIrbidJordan Department of Information Security Faculty of Information TechnologyUniversity of PetraAmmanJordan Software Engineering Department University of JeddahJeddahKSA Research and Innovation Department Skyline University CollegeP.O.Box 1797SharjahUAE IT Department-Al-Huson University College Al-Balqa Applied UniversityP.O.Box 50IrbidJordan

Social media networks are becoming essential to our daily activities,and many issues are due to this great involvement in our *** is a social media network issue,a global crisis affecting the victims and society as a *** results from a misunderstanding regarding freedom of *** this work,we proposed a methodology for detecting such behaviors(bullying,harassment,and hate-related texts)using supervised machine learning algo-rithms(SVM,Naïve Bayes,Logistic regression,and random forest)and for predicting a topic associated with these text data using unsupervised natural language processing,such as latent Dirichlet *** addition,we used accuracy,precision,recall,and F1 score to assess prior *** show that the use of logistic regression,support vector machine,random forest model,and Naïve Bayes has 95%,94.97%,94.66%,and 93.1%accuracy,respectively.

关键词： Cyberbullying social media naïve bayes support vector machine natural language processing LDA

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Transient Session Key Derivation Protocol for Key Escrow Prevention in Public Key Infrastructure 1st

Transient Session Key Derivation Protocol for Key Escrow Pre...

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1st EAI International Conference on Internet of Everything, IoECon 2022

作者： Nyangaresi, Vincent Omollo Abduljabbar, Zaid Ameen Maolood, Ismail Yaqub Sibahee, Mustafa A. Al Ma, Junchao Aldarwish, Abdulla J. Y. Faculty of Biological and Physical Sciences Tom Mboya University Homabay40300 Kenya Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah61004 Iraq Shenzhen Institute Huazhong University of Science and Technology Shenzhen518118 China – System Information Ministry of Higher Education and Scientific Research Erbil Iraq College of Big Data and Internet Shenzhen Technology University Shenzhen518118 China Computer Technology Engineering Department Iraq University College Basrah Iraq

ISBN: (纸本)9783031252211

The Internet of Things (IoT) devices have been deployed to realize smart environments such as smart cities, smart homes, smart health and smart grids. In these domains, the IoT devices collect and forward high volumes of sensitive and private data. It is therefore important that security schemes be developed to protect the exchanged data. In this regard, a myriad of authentication protocols have been developed over the recent past. However, these schemes deploy cryptographic primitives that result in extremely high communication, storage and computation complexities. In addition, some of these protocols still have numerous security and privacy issues that render them unsuitable for deployment in an IoT environment. For instance, untraceability, anonymity, key escrow problems and attack vectors such as de-synchronization and forgery attacks are frequently ignored. In this paper, a transient session key derivation protocol is developed to address some of these security and efficiency challenges. The security analysis executed shows that this protocol offers untraceability, device anonymity and perfect forward key secrecy. In addition, it is robust against de-synchronization, known secret key leakage, eavesdropping and forgery attacks. In terms of operational efficiency, this protocol incurs the lowest computation and communication complexities. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Authentication

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system Dynamics in Manufacturing Processes Modelling and Analysis 1

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3rd International Conference on Innovation in Engineering, ICIE 2024

作者： Litwin, Pawel Szymusik, Anna Department of Computer Science Faculty of Mechanical Engineering and Aeronautics Rzeszow University of Technology Al. Powstanców Warszawy 8 Rzeszów35-959 Poland

ISBN: (数字)9783031615825

ISBN: (纸本)9783031615818

The systems dynamics (SD) method allows analysis of the performance of complex systems over the full simulation period. It allows for prediction of the dynamics of systems containing many interdependent elements. However, due to the complexity of models, SD is most often used to map systems at an aggregate level with a low level of detail. This paper presents solutions to improve the process of detailed modelling and simulation of manufacturing systems. The integration of SD with value stream mapping (VSM) and the use of pattern models help streamline the modeling process and can contribute to increasing the SD application area in manufacturing process analysis. The case study illustrates the methodology for modeling and simulation and shows how SD can be used to improve the manufacturing process. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： system theory

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