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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Meeta Singh Poonam Nandal Deepa Bura Srishty Jindal Department of Computer Science & Engineering School of Engineering & Technology Manav Rachna International Institute of Research and Studies (Deemed to be University) Faridabad

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

This paper introduces a guide aimed at aligning academic learning with industry standards in Agile methodology and Scrum framework usage. Targeting students, educators, and industry practitioners, it addresses the prevalent gap between theoretical knowledge and practical *** with an overview of Agile's iterative approach, it delves into Scrum's key elements: sprint planning, daily stand-ups, reviews, and retrospectives. It acknowledges the disconnect between academic curricula and industry demands, especially in Agile contexts.

关键词： Industries Reviews Navigation Project management Planning Iterative methods Standards

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Innovative Urban Solutions with IoT-Driven Traffic and Pollution Control

Innovative Urban Solutions with IoT-Driven Traffic and Pollu...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Yogi, Kottala Sri Sharma, Avinash Dankan Gowda, V. Saxena, Rini Barua, Tarkeshwar Mohiuddin, Khalid Dept of Operations Symbiosis Institute of Business Management Hyderabad Symbiosis International University Pune Telangana Hyderabad India Chandigarh Engineering College Chandigarh Group of Colleges Jhanjeri Department of Computer Science and Engineering Punjab Mohali140307 India Bms Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India King Khalid University College of Business Department of Business Informatics PO Box-3247 Abha61471 Saudi Arabia

ISBN: (纸本)9798350382723

The Internet of Things (IoT) is rethinking the model for building the smart cities, and this paper makes just such a step in that direction. It then examines various ways of cutting urban pollution. Under increasing pressure from rising urbanization, smart cities represent a huge leap forward in regard to efficiency, scalability and livability. The key is IoTtechnology, which collects and analyzes data in real time and allows for more quick-witted and intelligent urban governance. This paper combines literature review, case study analysis and data-driven methods to focus on the opportunities offered by IoT applications for restructuring urban traffic systems and pollution control. This study explains what is missing in modern smart city initiatives, and offers a road map for bringing Internet of things solutions to municipal systems. The results can also serve as some answers to cope with the dilemma and bring more intelligence, cleanness, efficiency in making a city into a smart metropolis. The article ends by highlighting the significance of IoT for future urban development, and ideas for further research into this complex yet rapidly evolving area. © 2024 IEEE.

关键词： Internet of things

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Smart Road Damage Survey System Using Machine Vision and IoT technology

Smart Road Damage Survey System Using Machine Vision and IoT...

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Intelligent Cybernetics technology & Applications (ICICyTA), International Conference on

作者： Kusprasapta Mutijarsa Figo Agil Alunjati Fadhil Hidayat Ranna Kurnia Zulkifli Arsyad School of Electrical Engineering and Informatics Bandung Institute of Technology (ITB) Bandung Indonesia Department of Civil Engineering National Institute of Technology Bandung Indonesia Department of Computer and Informatics Engineering Bandung State Polytechnic Bandung Indonesia

Road is an essential transportation infrastructure to move people and goods to support economic prosperity. The efficiency, safety, security, and comfort of people have significant consequences by damage and defects on the road surface. A good-condition road ensures the land traffic runs well. This research is a collaboration between the Bandung institute of technology and the department of Public Works and Spatial Planning of West Java Province to develop a smart system for road damage surveys. The system was built by leveraging machine vision and IoT technologies to improve the performance of the road survey activities. The previous road damage detection, classification method, and pavement condition index (IKP) computation were performed semi-automatically. The proposed survey system developed in this work uses the Canny edge Detection algorithm and MobileNet V2 architecture to detect road defects automatically. The pavement condition index computation also improved by using the interpolation reduction factor. The system is designed into a four-layer architecture that consists of several functions and modules, i.e., the sensor system that includes a vision sensor, GPS and odometry, a hardware embedded system using a microcontroller, and machine learning to process the road condition image automatically, and a dashboard application to reveal the road condition on map. The performance resulted in accuracy, precision, recall, and F-score of the road damage detection are 54.5%, 24.2%, 78.9%, and 37%, respectively.

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Eye State Classification Using Ensemble Machine Learning Models and SMOTE on EEG Data

Eye State Classification Using Ensemble Machine Learning Mod...

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South Eastern European Design Automation, computer engineering, computer Networks and Social Media Conference (SEEDA-CECNSM)

作者： Maria Trigka Elias Dritsas Phivos Mylonas Industrial Systems Institute Athena Research and Innovation Center Patras Greece Department of Informatics and Computer Engineering University of West Attica Egaleo Greece

ISBN: (数字)9798331504489

ISBN: (纸本)9798331504496

Electroencephalography (EEG) data presents complex and high-dimensional signals, offering great potential for applications in various fields such as neurofeedback, clinical diagnostics, cognitive neuroscience, human-computer interaction (HCI), and beyond. Analyzing EEG signals requires expertise not only from neuroscience, but also from signal processing, machine learning (ML), and statistics to extract meaningful information from brain activity recordings. Specifically, the combination of EEG and ML can provide an advantage in addressing challenging classification tasks in these fields. The present study focuses on the classification of eye state (open or closed) using Ensemble ML models such as Random Forest (RF), Gradient Boosting (GB), AdaBoost, XGBoost, and LightGBM on EEG data. We apply the Synthetic Minority Over-sampling Technique (SMOTE) to address the class imbalance and conduct a comparative analysis of the models’ performance with and without SMOTE using 10-fold cross-validation across several metrics namely, Accuracy, Precision, Recall, F1-score, and the Area Under the Curve (AUC). The experimental results highlight the importance of addressing the class imbalance in EEG data to improve model performance.

关键词： Measurement Analytical models Accuracy Social networking (online) Brain modeling Boosting Electroencephalography Data models Time-domain analysis Random forests

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Performance and Security Analysis of the Diffie-Hellman Key Exchange Protocol

Performance and Security Analysis of the Diffie-Hellman Key ...

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International Workshop on Semantic Media Adaptation and Personalization (SMAP)

作者： Elias Dritsas Maria Trigka Phivos Mylonas Industrial Systems Institute Athena Research and Innovation Center Patras Greece Department of Informatics and Computer Engineering University of West Attica Egaleo Greece

ISBN: (数字)9798331504502

ISBN: (纸本)9798331504519

The Diffie-Hellman Key Exchange Protocol (DHKE) is a fundamental element of modern cryptographic systems, enabling secure key exchange over unsecured channels. The present research work aims to provide a comprehensive analysis of DHKE, with a dual focus on performance and security. We examine the protocol’s robustness against classical and emerging threats, particularly in the context of the Discrete Logarithm Problem (DLP) and the advent of quantum computing. Performance is evaluated by measuring execution times for key lengths of 512 and 1024 bits, each recorded over 100 iterations. The results are analyzed to provide insights into both computational efficiency and variability, with graphical representations to illustrate the findings. The study underscores the balance between security and performance in DHKE and offers recommendations for future enhancements to maintain cryptographic resilience in the face of advancing computational capabilities.

关键词： Performance evaluation Protocols Quantum computing Parallel processing Length measurement Software Robustness Computational efficiency Cryptography Resilience

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AI-based Object Detection for Assisting the Visually Impaired People 5

AI-based Object Detection for Assisting the Visually Impaire...

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5th International Conference on Mobile Computing and Sustainable informatics, ICMCSI 2024

作者： Sameer, Syed Madan, Parul Kannan, Sathish Upadhye, Vijay Jagdish Patil, Harshal Rajkumar, S. Campus Department of Science and Technology Karnataka Bengaluru560012 India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Saveetha Institute of Medical and Technical Sciences Department of VLSI Microelectronics Saveetha School of Engineering Tamil Nadu Chennai602105 India Parul Institute of Applied Sciences Parul University Research and Development Cell Department of Microbiology Gujarat Waghodia India Symbiosis Institute of Technology Maharashtra Pune412 115 India Hawassa University Faculty of Manufacturing Institute of Technology Department of Mechanical Engineering Ethiopia

ISBN: (纸本)9798350395235

In recent decades, vision problems have become more common. One significant barrier is that people who are visually impaired generally require assistance performing their daily tasks. As a result, developing a technological solution to this challenge is critical. Implementing an automatic object identification system can enable visually impaired people to roam independently. The study employs two Artificial Intelligence (AI) models, a Region-based Fully Convolutional Network (RFCN) and a Mask Region-based Convolutional Neural Network (Mask RCNN), to detect objects and assess their performance. The evaluation demonstrates RFCN's outstanding performance with an impressive Mean Average Precision (mAP) score of 0.825. The Raspberry Pi processor is used as a vital component in the process of hardware development. The RFCN model is integrated into the processor to aid in the identification of objects collected by the system's picamera. The detected objects are communicated to users via auditory feedback. The hardware system also includes an ultrasonic sensor, which provides distance information for the detected objects. This full hardware solution is specifically developed to help visually impaired people overcome mobility issues in both indoor and outdoor settings. The suggested system seeks to improve the autonomy and safety of visually impaired individuals in their day-to-day activities by seamlessly mixing advanced technologies such as AI and embedded systems. © 2024 IEEE.

关键词： Object detection

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Machine Learning Models with Neutrosophic Numbers for Network Anomaly Detection and Security Defense technology

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Neutrosophic Sets and Systems 2025年 83卷 50-64页

作者： Al-Khazraji, Hussein S. Alkhamees, Ahmed M. Al-Doori, Humam M. Metwaly, Ahmed A. Eassa, Mohamed Abdelhafeez, Ahmed Salama, Ahmed S. Nagm, Ahmad M. Department of Electrical Power Engineering Technologies Al-Hussein University College Karbala Iraq College of Health and Medical Technologies Department of Anesthesia Technologies Ahl Al Bayt University Karbala Iraq Department of Computer Engineering Techniques Al-Yarmok University College Diyala Iraq Department of Computer Science Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt Computer Science Department Faculty of Information Systems and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Engineering and Electronics Cairo Higher Institute for Engineering Computer Science and Management New Cairo Egypt

In the dynamic world of cybersecurity, strong solutions are necessary to safeguard intricate network systems. By looking at network anomaly detection and security protection, this study investigates how machine learning (ML) might increase digital infrastructure security. We assess how well critical ML approaches, such as ensemble approaches and supervised learning, identify anomalies and lessen risks. The examination of ML-based systems integration into comprehensive security frameworks places a strong emphasis on real-time monitoring and adaptive responses. Examples from real-world situations highlight how crucial ML is to improving network security. After, we apply different ML models to the real-world dataset. Then we use the single-valued Neutrosophic numbers (SVNNs) methodology to evaluate these ML models and select the best one. We use the multi-criteria decision-making (MCDM) approach to obtain the criteria weights and rank the ML models using the EDAS method. The results show that the random forest model is the best ML model under different evaluation matrices. © 2025, University of New Mexico. All rights reserved.

关键词： Risk assessment

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Design and implementation of obstacle detection system for powered wheelchairs 7

Design and implementation of obstacle detection system for p...

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7th South-East Europe Design Automation, computer engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2022

作者： Tatsis, Giorgos Koritsoglou, Kyriakos Fudos, Ioannis Karvounis, Evaggelos Votis, Konstantinos Tzovaras, Dimitrios Information Technologies Institute Centre for Research and Technology Hellas Ioannina Greece Information Technologies Institute Centre for Research and Technology Hellas Thermi57001 Greece University of Ioannina Dept of Computer Science and Engineering Ioannina Greece University of Ioannina Campus of Arta Department of Informatics and Telecommunications Arta Greece

ISBN: (数字)9798350398588

ISBN: (纸本)9798350398588

In this paper a detailed description of the design and implementation of a system capable of detecting various obstacles for wheelchairs is provided. This system is part of a larger project that its goal is to create intelligent applications for patients using powered wheelchairs. The system is implemented on an electric wheelchair using low-cost ultrasonic and infrared sensors. All tests took place in indoor environment inside our laboratories, and the performance of the system is found to be adequate for the purposes of our project. © 2022 IEEE.

关键词： Ultrasonic applications

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Enhancing the clinical relevance of haemorrhage prediction models in trauma

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Military Medical research 2024年第3期11卷 467-468页

作者： Sankalp Tandle Jared M.Wohlgemut Max E.R.Marsden Erhan Pisirir Evangelia Kyrimi Rebecca S.Stoner William Marsh Zane B.Perkins Nigel R.M.Tai Centre for Trauma Sciences Blizard InstituteQueen Mary University of LondonLondon E12ATUK The Royal London Hospital Barts Health NHS TrustLondon E11FRUK Academic Department of Military Surgery and Trauma Research and Clinical InnovationThe Royal Centre for Defence MedicineBirmingham B152WBUK Department of Electronic Engineering and Computer Science Queen Mary University of LondonLondon E14NSUK

We read with interest the recent systematic reviewaArtificial intelligence and machine learning for hemorrhagic trauma careoby Peng et al.[1],which evaluated literature on machine learning(ML)in the management of trau... 详细信息

关键词： Trauma Injury Blood transfusion Massive transfusion Prediction Artificial intelligence Machine learning

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Enhancing Secure Cross-Border Collaboration among Law Enforcement Agencies for Facial Biometric Search

Enhancing Secure Cross-Border Collaboration among Law Enforc...

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Electronic engineering, Information technology & Education (EEITE), International Conference in

作者： Kyriaki Miniadou Asterios Leonidis Georgios Th. Papadopoulos Constantine Stephanidis Institute of Computer Science (ICS) Foundation for Research and Technology - Hellas (FORTH) Heraklion Greece Department of Informatics and Telematics Harokopio University of Athens Athens Greece Department of Computer Science University of Crete Heraklion Greece

ISBN: (数字)9798350372878

ISBN: (纸本)9798350372885

Addressing the growing challenge of combating international crime involves the development of secure and efficient methodologies that allow Law Enforcement Agencies to exchange information seamlessly, without being hindered by time-consuming bureaucratic processes. In this context, we present a solution centered on facial biometric search methodologies. Our approach underscores the importance of employing accurate and reliable methods to assess image data similarity, particularly in the domain of facial images, which pose unique challenges due to subtle variations. We propose a comprehensive solution that harnesses hashing techniques and homomorphic encryption. By doing so, our approach ensures secure data exchange while safeguarding confidentiality and integrity. We firmly believe that our approach will substantially improve collaboration in law enforcement efforts and make significant contributions to global security.

关键词： Data privacy Law enforcement Scalability Education Collaboration Fingerprint recognition Regulation

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