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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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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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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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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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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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ICSGD-Momentum: SGD Momentum based on Inter-gradient Collision 22

ICSGD-Momentum: SGD Momentum based on Inter-gradient Collisi...

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22nd IEEE International Conference on Industrial informatics, INDIN 2024

作者： Zou, Weidong Cao, Weipeng Xia, Yuanqing Zhong, Bineng Li, Dachuan School of Automation Beijing Institute of Technology Beijing China Shenzhen China Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

ISBN: (纸本)9798331527471

Deep neural networks (DNNs) are widely used in fields like computer vision and natural language processing. A key component of DNN training is the optimizer. SGD-Momentum is popular in many DNN methodologies, such as ResNet and DenseNet, due to its simplicity and effectiveness. However, its slow convergence rate limits its use. To overcome this, we introduce inter-gradient collision into SGD-Momentum, inspired by the elastic collision model in physics. This new method, called ICSGD-Momentum, aims to improve convergence. We provide theoretical proof of convergence and establish a regret bound for ICSGD-Momentum. Experiments on benchmarks including function optimization, CIFAR-100, ImageNet, Penn Treebank, COCO, and YCB-Video show that ICSGD-Momentum accelerates training and enhances the generalization performance of DNNs compared to optimizers like SGD-Momentum, Adam, RAdam, Adabound, and AdaBelief. © 2024 IEEE.

关键词： Deep neural networks

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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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Multi-objective VNF Placement Optimization with NSGA-III 1st

Multi-objective VNF Placement Optimization with NSGA-III

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1st International Conference on Advances in Computing research, ACR’23

作者： Bekhit, Mahmoud Fathalla, Ahmed Eldesouky, Esraa Salah, Ahmad Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia Kent Institute Australia Melbourne Australia Department of Mathematics Faculty of Science Suez Canal University Ismailia Egypt Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia College of Computers and Informatics Zagazig University Zagazig Egypt College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman

ISBN: (纸本)9783031337420

With the rising acceptance of virtual network functions (VNFs) as a replacement for traditional network functions, the optimal placement of VNFs has become a crucial task for ensuring constant performance within constrained resources. This research investigates the challenge of mapping and scheduling VNFs as a multi-objective optimization problem. The state-of-the-art methods have focused on optimizing the mapping or scheduling of VNFs while considering one or two objectives continuously. In this study, we aim to optimize two objectives: minimizing the link capacity cost and maximizing the average resource utilization of each virtual machine. To address this problem, an adaptation of the NSGA-III optimization algorithm is proposed and thoroughly evaluated through a set of experiments. In terms of objective values, the results outlined that the proposed method outperformed the other methods of comparison by a significant margin. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Mapping

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Cross-Layer Distributed Attack Detection Model for the IoT

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International Journal of Ambient Computing and Intelligence 2022年第1期13卷

作者： Ahmed, Hassan I. Nasr, Abdurrahman A. Abdel-Mageid, Salah M. Aslan, Heba K. Informatics Department Electronics Research Institute Cairo Egypt Computer Engineering Faculty of Engineering Al-Azhar University Cairo Egypt Computer Engineering Department College of Computer Science and Engineering Taibah University Saudi Arabia Center of Informatics Science Faculty of Information Technology and Computer Science Nile University Egypt

The security of IoT that is based on layered approaches has shortcomings such as the redundancy, inflexibility, and inefficiently of security solutions. There are many harmful attacks in IoT networks such as DoS and DDoS attacks, which can compromise the IoT architecture in all layers. Consequently, cross layer approach is proposed as an effective and practical security defending mechanism. Cross-layer distributed attack detection model (CLDAD) is proposed to enhance security solutions for IoT environments. CLDAD presents a general detection method of DDoS in sensing layer, network layer, and application layer. CLDAD is based on big data analytics techniques, which enable the detection process to be performed in a distributed way, so the model can detect DDoS attacks in any layer on-the-fly and the model support the scalability of the IoT environment. CLDAD is tested based on three datasets, namely artificial jamming attack dataset, BoT-IoT dataset, and BoT-IoT-based HTTP. The results showed that the proposed model is efficient in detecting attacks in the three layers of the IoT. Copyright © 2022, IGI Global.

关键词： Big data

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Impact of Expert academic Teaching Quality and its Performance Based on BiLSTM-Deep CNN Network 7

Impact of Expert Academic Teaching Quality and its Performan...

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7th International Conference on Electronics, Communication and Aerospace technology, ICECA 2023

作者： Shukla, Tushar Dhar Acharjee, Purnendu Bikash Chethan, C. Thulasimani, T. Sindhu, M. Sharma, Sanjiv University Institute of Teachers Training & Research Chandigarh University Mohali India CHRIST University Pune India Sri Venkateshwara College of Engineering Department of Information Science and Engineering Bengaluru India Bannari Amman Institute of Technology Department of Mathematics Tamilnadu Sathyamangalam India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai India Madhav Institute of Technology and Science Department of IT Madhya Pradesh Gwalior India

ISBN: (纸本)9798350340600

Undergraduate and postgraduate students from eight different departments at a UK institution participated in organized conversations about the impact of teachers' research activities on their education. In both samples, positive responses greatly outnumbered negative ones. There was an increase in positive feedback on professors' research when the overall quantity and quality of research in a specific field (as measured by research Assessment Exercise [RAE] ratings) improved. Undergraduate samples with higher RAE scores were more likely to have negative feedback on research than graduate student samples. Both graduate and undergraduate students agreed that lecturers' research increased the instructor's credibility, relevance, and knowledge, as well as piqued and maintained their own interest, engagement, and drive. Data processing, feature selection, and model training are the first steps in the proposed approach. The data are changed from their raw form into a form suitable for academic use during the data pre-processing phase. They are employing Information Gain and Symmetric Uncertainty for feature selection. Following the feature selection process, the models are trained using BiLSTM-CNN. Both the BiLSTM and the CNN methods are inferior to the proposed method. © 2023 IEEE.

关键词： Long short-term memory

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