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4th International Conference on Advances in computer technology, Information Science and Communications, CTISC 2022

作者： Li, MingYang Liang, JiaRui Yan, PengCheng Tian, XiaoLin Macau University of Science and Technology School of Computer Science and Engineering Faculty of Innovation Engineering China Macau University of Science and Technology Lunar and Planetary Science Laboratory Space Science Institute China

ISBN: (数字)9781665458726

ISBN: (纸本)9781665458726

This paper proposes an algorithm(system) to automatically mark and create a data set of Mars craters for deep learning based on the existing coordinate information. The main purpose of this automatic processing system is to solve the error problem and low efficiency caused by manual marking. To make such system, we need to know something about planetary science, such as the way to get a full view of a planet, the differences between DEM (Digital Elevation Model) image and TIR (Thermal Infrared image) and the way maps are *** this paper, the crater is used as the detection feature, I chose Mars DEM image as basic dataset to develop this system, and used YOLOv5 model to test the dataset, the results show that this system can quickly and successfully produce the crater data set that can be used for the training and detection of neural network object detection. Then Mars Thermal Infrared Image was used to expand my system. It also can create dataset that can be used for object detection model but haven't been proved by any object detection model yet. In this report, the details of how to achieve automatically generating training datasets and some essential problems will be introduced. © 2022 IEEE.

关键词： Object detection

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An Review of the Effective Energy Consumption Within the Green IT and Green Energy Strategies 14

An Review of the Effective Energy Consumption Within the Gre...

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14th International Scientific and Practical Conference on Environment. technology. Resources, ETR 2023

作者： Mamchur, Dmytro Peksa, Janis Department of Information Technology Turiba University Engineering and Electronics Department Kremenchuk Mykhailo Ostrohradskyi National University RigaLatvia Kremenchuk Ukraine Department of Information Technology Institute of Information Technology Riga Technical University Faculty of Computer Science and Information Technology Turiba University Riga Latvia

ISBN: (纸本)9781713874881

The paper provides analysis on the current state of the art in the field of green technologies, including green information technologies, and methods aimed to achieve effective energy consumption goals. Recent advances in the field summarized in prospective publications, were analysed. Modern Green IT and Green Energy strategies, aimed to reduce power consumption, environmental influence and improve sustainability, which assume the use of Cloud Technologies, renewable energy sources, proper recycling and reuse of devices and resources, were singled out. Finally, recommendations for IT and industrial companies aimed helping to achieve green strategies' goals, were formulated. © 2023 Rezekne Higher Education Institution. All rights reserved.

关键词： Green computing

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Deep Learning Model for Detecting Emotions Through Pipeline

Deep Learning Model for Detecting Emotions Through Pipeline

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International Conference on Advanced Technologies for Communications, ATC

作者： Afshan Fatima Masrath Parveen Vijender Kumar Solanki Ngoc Linh Nguyen Computer Science & Engineering CMR Institute of Technology Hyderabad Telangana India Computer Science &Engineering VJ Institute of Technology Hyderabad Telangana India Computer Science & Engineering Stanley College of Engineering and Technology for women Hyderabad Telangana India Faculty of Applied Science VNU-International School Hanoi Vietnam

ISBN: (数字)9798350353983

ISBN: (纸本)9798350353990

Human emotions, a pervasive psychological phenomenon, significantly influence individuals' daily experiences and mental health. In today's society, emotional well-being has become a major focus of concern. Facial expressions convey a diverse array of emotions, including natural, joy, sorrow, revulsion, terror, rage, astonishment, and disdain. This research introduces an innovative real-time emotion detection system that analyzes facial images using a multi-kernel approach within a deep learning framework. A notable contribution is the implementation of a Support Vector Machine (SVM) classifier with multiple kernel functions, emphasizing the improved accuracy and decreased processing time achieved by the Polynomial Kernel. This model demonstrates substantial enhancements in precision and efficiency compared to existing single-kernel methods, making it ideal for real-time applications. The study explores the effectiveness of emotional indicators derived from social media platforms, such as chatbots, messages, photographs, and facial expressions. The paper proposes a deep learning-based workflow for emotion recognition through facial expressions, utilizing a multi-kernel approach. The model incorporates Linear, Radial Basis Function (RBF), and Polynomial kernels, with the Polynomial Kernel exhibiting superior performance, attaining the highest cross-validation accuracy of 77.97% on the FER-2013 dataset. This research provides a thorough assessment of the model's accuracy and computational efficiency, demonstrating its competitive edge over current emotion recognition techniques.

关键词： Support vector machines Deep learning Emotion recognition Accuracy Social networking (online) Computational modeling Face recognition Polynomials Real-time systems Kernel

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DAS benchmarking methodology based on FWZIC II and FDOSM II to support industrial community characteristics in the design and implementation of advanced driver assistance systems in vehicles

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Journal of Ambient Intelligence and Humanized Computing 2023年第9期14卷 12747-12774页

作者： Mahmoud, U.S. Albahri, A.S. AlSattar, H.A. Zaidan, A.A. Talal, M. Mohammed, R.T. Albahri, O.S. Zaidan, B.B. Alamoodi, A.H. Qahtan, Sarah Baghdad Iraq Baghdad Iraq Baghdad Iraq Department of Computing FSKIK Universiti Pendidikan Sultan Idris Tanjung Malim35900 Malaysia Faculty of Engineering and IT British University in Dubia Dubai United Arab Emirates Parit Raja Johor Batu Pahat86400 Malaysia Department of Computer Science Faculty of Science Komar University of Science and Technology Sulaymaniyah Iraq Computer Techniques Engineering Department Mazaya University College Nasiriyah Iraq Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin64002 Taiwan College of Health and Medical Technology Middle Technical University Baghdad Iraq Department of Business Administration College of Administrative Science The University of Mashreq Baghdad Iraq

This study proposed a novel methodology of data acquisition systems (DASs) benchmarking based on fuzzy-weighted zero-inconsistency (FWZIC II) and fuzzy decision by opinion score method (FDOSM II), which are applied in an intuitionistic fuzzy set (IFS) context and account for hesitation when benchmarking DASs, to support industrial community characteristics in the design and implementation of advanced driver assistance systems in vehicles. The proposed methodology comprises two consecutive phases. The first phase involves constructing a decision matrix based on the intersection of the DAS alternatives and criteria. The second phase (development phase) proposes the formulation of a novel FWZIC II to weight the criteria and the formulation of a novel FDOSM II to benchmark DASs. Fourteen DASs were benchmarked based on the 15 DAS criteria, which included seven sub-criteria for ‘comprehensive complexity assessment’ and eight sub-criteria for ‘design and implementation’, which had a significant effect on the DAS design when implemented by industrial communities. A systematic ranking and sensitivity analysis were conducted to demonstrate that the benchmarking results were subject to systematic ranking, as indicated by the high correlations across all described scenarios of changing criteria weight values. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Benchmarking

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Synthetic Batik Pattern Generator Using Wasserstein Generative Adversarial Network with Gradient Penalty 6

Synthetic Batik Pattern Generator Using Wasserstein Generati...

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6th International Seminar on Research of Information technology and Intelligent Systems, ISRITI 2023

作者： Andriadi, Kus Heryadi, Yaya Lukas Suparta, Wayan Sonata, Ilvico Binus Graduate Program Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Jakarta12930 Indonesia Institut Teknologi Nasional Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta Yogyakarta55281 Indonesia

ISBN: (纸本)9798350358346

Batik is an Indonesian world cultural heritage. Batik consists of many kinds of patterns depending on where the batik comes from, Batik-making techniques continue to develop along with technology development. Among the batik making techniques that are widely used are hand-written, stamping, and printing. Batik motifs have been widely used as research material, especially in the field of artificial intelligence. The diverse appearance of batik motifs has attracted many researchers to carry out research on making synthetic batik patterns, one of which uses a Generative Adversarial Network. This paper presents a synthetic batik pattern model based on the Wasserstein Generative Adversarial Network with Gradient Penalty. This model has been proven to create new synthetic batik patterns quite well and almost identical with images provided in the dataset, with the notes if the dataset provided is large. © 2023 IEEE.

关键词： Generative adversarial networks

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An Enhanced Secure Authentication Scheme for the Internet of Things

An Enhanced Secure Authentication Scheme for the Internet of...

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2023 International Conference on Sustainable Emerging Innovations in engineering and technology, ICSEIET 2023

作者： Neetu Srivastava, Animesh Barik, Lal Bihari Dhondiyal, Shiv Ashish College of Smart Computing Coer University Roorkee India Graphic Era Hill University Department of Computer Science & Engineering Dehradun India King Abdulaziz University Faculty of Computing and Information Technology in Rabigh Department of Information Systems Saudi Arabia Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350329186

The Internet of Things (IoT) has led to the proliferation of interconnected devices, including smart appliances and industrial sensors. Nevertheless, the rapid expansion of the IoT ecosystem has given rise to apprehensions regarding security, as compromised devices have the potential to result in data breaches and cyber assaults. This paper presents an enhanced secure authentication scheme for the Io'T, which aims to tackle the issues above by building upon existing authentication methodologies. The multi-step system commences with an initial setup phase wherein each loT device is assigned a unique device ID and a confidential pre-shared key. A secure key exchange protocol generates a session key for each authenticated device. The authentication server generates a random attempt to join the network or execute specific tasks. The machine then signs the data with its private key to authenticate the challenge. The server verifies the answer by comparing it with the public key signature of the device. The proposed methodology enables devices to update their session keys periodically to safeguard against potential attacks. © 2023 IEEE.

关键词： Authentication

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Simulation Secure MQTT Protocol Based on TLS in IoT-Fog Computing Environment 4th

Simulation Secure MQTT Protocol Based on TLS in IoT-Fog Comp...

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4th Doctoral Symposium on Computational Intelligence, DoSCI 2023

作者： Kadhim, Ola Najah Ketab, Ahmed Saud Obaid, Ahmed J. Albermany, Salah Abdulhadi Raheem, Ahmed R. Hussien, Naseer Ali Faculty of Computer Science and Mathematics University of Kufa Najaf Iraq College of Engineering Technology National University of Science and Technology Dhi Qar Iraq Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Thi-Qar Iraq

ISBN: (纸本)9789819937158

Fog computing is a way to run a computer that sends data from IoT devices to a group of nodes that get it in real time. These nodes do real-time processing of the data that they get, with the least amount of response time. The nodes send summaries of their analysis to the server regularly. A server-based application then looks at the data that has been sent from the different nodes and tries to come up with actionable information. Fog architecture needs more than just computer power. It needs to be able to communicate quickly between IoT devices and servers. In this paper, a secure Message Queuing Telemetry Transport Protocol (MQTT) broker in fog computing is described as an entity that acts as an intermediate to facilitate communication between MQTT clients, to receive messages that have been published by clients, to filter the messages based on topic, and to disseminate them to subscribers. The MQTT protocol publish/subscribe data transmission is encrypted using Transport Layer Security (TLS) in the proposed method. This has the additional advantages of maintaining network performance, improving channel utilization, lowering the number of loose packets in comparison with the case study that was carried out without TLS, and increasing network latency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Internet of things

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A Deep Learning Approach for Locating Microalgae in Image Captured by Smartphone and Digital Microscope Camera 8

A Deep Learning Approach for Locating Microalgae in Image Ca...

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Joint 8th International Conference on Digital Arts, Media and technology and 6th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2023

作者： Kaewman, Nitiphong Charoenkwan, Phasit Yana, Jinnapat Suanoi, Phon-Ubon Duangjan, Kritsana Pekkoh, Jeeraporn Pumas, Chayakorn Chiang Mai University Faculty of Science Department of Biology Chaing Mai Thailand College of Arts Media and Technology Chaing Mai University Chaing Mai Thailand Chulalongkorn University Faculty of Engineering Department of Computer Engineering Bangkok Thailand Science and Technology Research Institute Chiang Mai University Chaing Mai Thailand Chiang Mai University Faculty of Science Department of Biology Chiang Mai Thailand

ISBN: (纸本)9798350396546

The utilization of microalgae as a bioindicator for water quality assessment has traditionally relied on the expertise of morphological identification. With the advent of technological advancements, particularly the smartphone camera, there has been increasing interest in using it as a tool for various fields of microbiological and medical diagnostics, including image analysis of microalgae. Despite the growing popularity of smartphone-based image analysis, the image quality of smartphone cameras for detecting microalgae has yet to be thoroughly evaluated. Therefore, the purpose of this study was to investigate the suitability of smartphone-based camera for collecting microalgae images and compare it to that of a digital microscope camera. Additionally, the effect of different microscopic magnifications at 100x and 400x on model performance was thoroughly studied. Four models, YOLOv5, RetinaNet, EfficientDet, and Faster-RCNN, were trained on microalgae image datasets taken by both a smartphone camera (SC) and a digital microscope camera (DMC) under 100x and 400x magnification. A total of 11,608 images were divided into three parts: train (70%), validation (10%), and test (20%), with the test dataset separated into four parts for each device and magnification. The results of the study indicated that Faster-RCNN was the best model for microalgae detection, with the highest average precision (AP0.5) values of 0.60, 0.84, 0.91, and 0.96 at 100x magnification of SC and DMC and 400x magnification of SC and DMC, respectively. The comparison between the image quality of SC and DMC at each magnification revealed that the SC was less suitable for microalgae identification, particularly at 100x magnification. Conversely, the 400x magnification of SC had the potential to identify microalgae through the use of smartphone-based photography, as well as digital microscope camera. © 2023 IEEE.

关键词： Microalgae

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Efficient Design of a Single-Balanced Diode Mixer for 5G Communication Systems 5

Efficient Design of a Single-Balanced Diode Mixer for 5G Com...

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5th International Conference on Computing, Electronics and Communications engineering. iCCECE 2022

作者： Sellidj, Abdelmounim Gaoua, Said Yagoub, Mustapha C.E. University of Science and Technology H. Boumedienne Faculty of Electronics and Computer Science Algiers Algeria University of Ottawa School of Electrical Engineering and Computer Science Ottawa Canada

ISBN: (数字)9781665489805

ISBN: (纸本)9781665489805

This work discusses the design of a broadband single-balanced down converter mixer, operating in the 5G NR FR2 bands of 24.25-29.5 GHz. With an IF frequency of 250 MHz, the mixer presents excellent port-To-port isolation over almost all the targeted band, i.e., 94.6 dB, 72.6 dB, and 25 dB for LO-IF, RF-IF, and RF-LO ports, respectively. It also exhibits low conversion loss (7.49 dB) and noise figure (with a maximum of 6.38 dB and 6.29 dB for double-and single-sideband, respectively). Furthermore, it displays a good linearity with a 1-dB compression point of 10.5 dB as well as input/output third order intermodulation products (IIP3/OIP3) of 18.68 dBm and 11.03 dBm, respectively. Such performance made the proposed design a suitable candidate for 5G applications. © 2022 IEEE.

关键词： Mixers (machinery)

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Streamlining Attendance with Voice Recognition via Gaussian Mixture Model

Streamlining Attendance with Voice Recognition via Gaussian ...

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2024 International Conference on IT and Industrial Technologies, ICIT 2024

作者： Ali, Noor Ali, Khadija Ali, Fatimah Ali, Aaminah Ali, Nisar Ali, Raja Hashim A-levels Lahore Grammer School 55 Main Gulberg Lahore54660 Pakistan Department of Software Engineering NUST College of Electrical and Mechanical Engineering Islamabad43701 Pakistan Department of Artificial Intelligence Faculty of Computer Science & Engineering GIK Institute of Engineering Sciences & Technology Topi23460 Pakistan Department of Business University of Europe for Applied Sciences Potsdam14469 Germany Faculty of Engineering and Applied Science University of Regina ReginaS4S0A2 Canada

ISBN: (纸本)9798331504465

Voice recognition systems are crucial because they allow seamless human-computer interaction and improve accessibility for users of all abilities. The use of these technologies in hands-free control, language translation, virtual assistants, transcription services, and hands-free control is revolutionising how we engage with technology and enhancing convenience and productivity in general. Several attendance systems based on voice recognition exist, but we wanted to deploy an attendance system with a good graphical user interface specifically for students of GIK Institute. For this purpose, we wanted to make a user-friendly and accurate voice recognition system based and trained on self-provided data of ten students. This study introduces an AI-driven attendance system, which demonstrates high efficiency and accuracy in identifying students’ daily class attendance. To achieve this, the Gaussian Mixture Model approach was employed. The paper also delves into the utilization of libraries and methods, encompassing the training and validation of well-known machine learning models. Additionally, the system’s performance, its strengths, weaknesses and potential areas for improvement are also discussed in the study. © 2024 IEEE.

关键词： Students

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