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International Conference on informatics and Computing (ICIC)

作者： Re. Miracle Panjaitan Budi Darma Setiawan Department of Informatics Engineering Faculty of Computer Science Brawijaya University Malang Indonesia

Crowdsourcing is becoming a trending concept used by many organizations, without being limited to a particular field. As of 2018, crowdsourcing was used by 85% of the largest worldwide companies. This trend of crowdsourcing would be increasing as it offers numerous advantages to companies in achieving their goals, such as the capability of obtaining data and information from the crowd. The crowd-provided data and information contain the essence of human ingenuity, which enables companies to do problem-solving with a human-centered approach. The more contributors participate, the more likely the crowdsourcing program will succeed. Several studies give different lists of factors that influence the participation rate of the crowd. However, there’s a lack of discussion about the factors and it leads to a situation where organizations need to examine many factors when developing a crowdsourcing platform to get great crowd engagement. Therefore, this study conducted a Systematic Literature Review (SLR) to perform screening and analysis of preceding research systematically. The factors in each of these literatures are generated by considering the case of the study or the region of the people related to the research. This diversity helps identify the factors influencing contributor participation in data crowdsourcing from research all over the world. A study reflecting on these research findings would help answer research questions thoroughly. This study found that there are 19 factors that positively influence contributor participation in data crowdsourcing out of 26 factors found in the 16 selected documents.

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Space Division Multiple Access for Cellular V2X Communications

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computers, Materials & Continua 2022年第10期73卷 1195-1206页

作者： Doaa Sami Khafaga Mohammad Zubair Khan Muhammad Awais Javed Amel Ali Alhussan Wael Said Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science and Information Taibah University Madinah Saudi Arabia Department of Electrical and Computer Engineering COMSATS University Islamabad45550Pakistan Department of Computer Science Faculty of Computers and InformaticsZagazig UniversityZagazig44511Egypt

Vehicular communication is the backbone of future Intelligent Transportation Systems(ITS).It offers a network-based solution for vehicle safety,cooperative awareness,and traffic management *** safety applications,Basic Safety Messages(BSM)containing mobility information is shared by the vehicles in their neighborhood to continuously monitor other nearby vehicles and prepare a local traffic *** are shared using mode 4 of Cellular V2X(C-V2X)communications in which resources are allocated in an ad hoc ***,the strict packet transmission requirements of BSM and hidden node problem causes packet collisions in a vehicular network,thus reducing the reliability of safety ***,as vehicles choose the transmission resources in a distributed manner in mode 4 of CV2X,the packet collision problem is further *** paper presents a novel solution in the form of a Space Division Multiple Access(SDMA)protocol that intelligently schedules BSM transmissions using vehicle position data to reduce concurrent transmissions from hidden node *** proposed protocol works by dividing road segments into clusters and *** sub-frames are allocated to a cluster and these sub-frames are reused after a certain *** a cluster,sub-channels are allocated to *** implement the proposed SDMA protocol and evaluate its performance in a highway vehicular *** results show that the proposed SDMA protocol outperforms standard Sensing-Based Semi Persistent Scheduling(SB-SPS)in terms of safety range and packet delay.

关键词： ITS vehicular network cellular V2X SDMA

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Estimating finger joint angles by surface EMG signal using feature extraction and transformer-based deep learning model

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Biomedical Signal Processing and Control 2024年第PartB期87卷

作者： Putro, Nur Achmad Sulistyo Avian, Cries Prakosa, Setya Widyawan Mahali, Muhammad Izzuddin Leu, Jenq-Shiou Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taiwan Department of Computer Science and Electronics Faculty of Mathematics and Natural Sciences Universitas Gadjah Mada Indonesia Department of Electronics and Informatics Engineering Faculty of Engineering Universitas Negeri Yogyakarta Indonesia

Human-machine interfaces frequently use electromyography (EMG) signals. Based on previous work, feature extraction has a great deal of influence on the performance of EMG pattern recognition. Furthermore, the Deep Learning method is supposed to increase performance and not depend on feature engineering. However, directly processing raw signals will require a higher computation rate. This study proposed a new method that combines feature extraction and Deep Learning to address those issues while improving performance, reducing architecture size, and producing a more representative output. The proposed architecture employs the Transformer model as the backbone to get the correlation between elements and focus on the important information for estimating the flexion-extension of finger joint angles. This study uses experiment three of the NinaPro (Non-Invasive Adaptive Hand Prosthetics) DB5 dataset. Each experiment produces 16 Surface EMG data streams from two Myo Armbands devices representing 22 finger joint angles as output. This study compares the windowing process, feature extraction, execution time, and results with previous studies. The results show that the proposed model outperforms the previous study, from 0.957 in the previous study to become 0.970 in this study for the R-Square score. This result is obtained using 100 data points for the windowing process and Median Frequency for the best feature extraction method. © 2023

关键词： Feature extraction

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Improved Gradient-Based Optimizer for solving real-world engineering problems 4

Improved Gradient-Based Optimizer for solving real-world eng...

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4th IEEE Middle East and North Africa COMMunications Conference, MENACOMM 2022

作者： Shehab, Mohammad Tarawneh, Omar Abusalem, Hani Shannag, Fatima Al-Omari, Walaa Amman Arab University Faculty of Computer Sciences and Informatics Amman Jordan University of South Carolina Aiken Department of Computer Science Engineering and Mathematics Aiken United States

ISBN: (纸本)9781665494113

Gradient-based optimizer (GBO) is one of the most promising metaheuristic algorithms, where it proved its efficiency in various fields. GBO combine two major search mechanisms population-based and gradient-based Newton. Thus, it has a strong ability in global search. However, it suffers from dealing with local search problems. In this paper, a new version introduces which integrates the feature of Simulating annealing method (SA) with the GBO (GBOSA) to enhance the local search technique. The proposed GBOSA has been compared with various popular algorithms and improved variants on a set of real-world engineering problems. The experiment results show that GBOSA outperformed the other algorithms in the literature. © 2022 IEEE.

关键词： Simulated annealing

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Adversarial-aware Machine Learning for Enhancing Smart Grid Security 25

Adversarial-aware Machine Learning for Enhancing Smart Grid ...

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25th International Middle East Power System Conference, MEPCON 2024

作者： Sodiq, Yusuf Ademola Gaber, Tarek Amin, Reham Jimenez-Aranda, Angel Galeela, Mohamed The University of Salford School of Science Engineering & Environment Manchester United Kingdom Suez Canal University Faculty of Computers and Informatics Computer Sciences Department Ismailia Egypt The University of Salford Centre for Sustainable Innovation Salford Business School Manchester United Kingdom TNEI Service LTD Manchester United Kingdom Cairo University Faculty of Engineering Electrical Power Engineering Department Egypt

ISBN: (纸本)9798350379648

Smart grid systems are essential components of modern power infrastructures, where machine learning has found wide-ranging applications, particularly in the development of intrusion detection systems. However, such systems remain vulnerable to adversarial attacks, including data poisoning techniques like label flipping attack. This paper proposes a novel, adversarial-aware machine learning-based intrusion detection system that is robust against label flipping attacks, thereby enhancing the integrity and reliability of the energy distribution network. The proposed solution was evaluated using the ICS cyber-attack dataset from the University of Queensland, with multiple models undergoing progressive testing. Initially, the base model employed three key algorithms: Random Forest, K-Nearest Neighbors, and XGBoost. These models were subjected to simulated label flipping attacks with varying intensities (10%-50%), which caused a significant decline in performance. To counter this, a countermeasure technique was integrated into the system, successfully restoring model accuracy and achieving a consistent accuracy of 75.96% across all attack intensities. Among the algorithms, Random Forest demonstrated the greatest resilience, showing a notable recovery after mitigation. This study underscores the critical role of preventive defense strategies and robust machine learning algorithms in safeguarding smart grids against cyber threats. © 2024 IEEE.

关键词： Adversarial machine learning

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Triple des Cryptography Based on Hash Function and DSA for Digital Certificate Authentication

Triple des Cryptography Based on Hash Function and DSA for D...

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2022 International Seminar on Application for Technology of Information and Communication, iSemantic 2022

作者： Rachmawanto, Eko Hari Budi Handoko, Lekso Umam, Chaerul Jatmoko, Cahaya Ali, Rabei Raad Dian Nuswantoro University Faculty of Computer Science Department of Informatics Engineering Semarang Indonesia National University of Science and Technology Nasiriyah Iraq

ISBN: (数字)9781665488396

ISBN: (纸本)9781665488396

Digital certificates are digital files that are conventionally used as proof of participation or a sign of appreciation owned by someone. Cryptographic authentication method using digital signatures that are used as confidential evidence and validation of digital certificate ownership. Digital signatures use cryptographic algorithms such as one-way hashes, non-symmetric key cryptography, and symmetric key cryptography. So this study uses the proposed algorithm used, namely SHA-256, DSA & 3DES. The SHA-256 algorithm is used because of its high security, while the DSA algorithm is a special standard for digital signatures with 2 (two) functions, namely signing & verifying, and the last is the 3DES algorithm as double security which guarantees 3 (three) times the encryption process and description. By implementing a digital signature using the SHA256 hash method, the DSA signing & verifying method, and the 3DES cryptographic algorithm, digital certificates can avoid editing threats, and ensure authentication security and validation of the validity of the file, so that it can detect the original digital certificate. In 10 experiments, it is known that the digital signature method with the SHA-256, DSA and 3DES algorithms has an average run time speed of 0.0518186331 seconds for the signing method and 0.1321175146 seconds for the verify method. © 2022 IEEE.

关键词： Authentication

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Comparison of *** and Spring Boot in Web Development 15

Comparison of *** and Spring Boot in Web Development

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15th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2023

作者： Novac, Ovidiu Constantin Ghiurau, David Novac, Mihaela Cornelia Gordan, Cornelia Emilia Oproescu, Mihai Bujdoso, Gyongyi Mihalca, Vlad Ovidiu University of Oradea Electrical Engineering and Information Technology Faculty Department of Computers and Information Technology Oradea Romania University of Oradea Management in Information Technology Electrical Engineering and Information Technology Faculty Oradea Romania University of Oradea Electrical Engineering and Information Technology Faculty Department of Electrical Engineering Oradea Romania University of Oradea Electrical Engineering and Information Technology Faculty Department of Electronics and Telecommunications Oradea Romania University of Pitesti Faculty of Electronics Communication and Computer Science Department of Electronics Computers and Electrical Engineering Pitesti Romania University of Debrecen Faculty of Informatics Department of Computer Science and Library and Information Science Debrecen Hungary School of Engineering Sciences University of Oradea Electrical Engineering and Information Technology Faculty Oradea Romania

ISBN: (纸本)9798350321388

In this paper we present two types of server implementation, we will look at the advantages and disadvantages of each technology. *** server application, built with ***, a single threaded server which will treat the requests in an asynchronous manner, with a non-blocking principle, whilst Spring Boot is a more robust and more common approach of a server technology with the benefits of having a strongly typed architecture and multiple threads available. These types of application will behave the same, making almost impossible to tell the difference between a *** and a Spring Boot web server. © 2023 IEEE.

关键词： Node.Js non-blocking single-multi threaded Spring Boot web server

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Fog Computing-based Real-Time Emotion Recognition using Physiological Signals

Fog Computing-based Real-Time Emotion Recognition using Phys...

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International Conference on Advanced Communication Technology (ICACT)

作者： Omur Fatmanur Erzurumluoglu Kadir Ozlem Asli Tuncay Atalay Ozgur Atalay Gökhan Ince Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey Faculty of Textile Technologies and Design Istanbul Technical University Istanbul Turkey

ISBN: (数字)9791188428137

ISBN: (纸本)9798331507602

Emotion recognition based on physiological signals has become a crucial area of research in affective computing and human-computer interaction, with applications in smart homes, workplaces, educational institutions, healthcare, and entertainment. In this study, a real-time emotion recognition system utilizing fog computing architecture was developed by considering the challenges of latency, total response time, resource usage, and security in IoT environments. The random forest machine learning model was trained with time-based statistical features by using the DREAMER dataset. Even though the model achieved an accuracy of 84.21% with 104 features, to meet real-time performance requirements, the system was optimized to calculate 24 features, maintaining a commendable accuracy of 79.70%. Extensive experiments demonstrated the superior performance of fog computing compared to edge and cloud computing in terms of latency, queuing delay, jitter, and most importantly total response time. The results highlight the proposed system’s ability to support multiple users simultaneously.

关键词： Emotion recognition Accuracy Computational modeling Smart homes Signal processing Brain modeling Real-time systems Physiology Time factors Edge computing

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Medical Image Watermarking Using Slantlet Transform and Particle Swarm Optimization 6th

Medical Image Watermarking Using Slantlet Transform and Part...

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6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Rachmawanto, Eko Hari Dawd, Lahib Nidhal Sari, Christy Atika Ali, Rabei Raad Al-Dayyeni, Wisam Subhi Jubair, Mohammed Ahmed Informatics Engineering Computer Science Faculty Dian Nuswantoro University Semarang50131 Indonesia Department of Computer Techniques Engineering Dijlah University College Baghdad Iraq National University of Science and Technology Thi-Qar Iraq Department of Computer Technical Engineering College of Information Technology Imam Ja’afar Al-Sadiq University Al-Muthanna66002 Iraq

ISBN: (纸本)9789819930098

Image quality can be increased to achieve the best imperceptibility without losing its robustness which is one of the benefits of using optimal schema as the process of embedding secret messages originating from PSO. The proposed technology uses improved SLT transient localization as an improved version of discrete wavelet transform (DWT). In addition, SLT is a multi-line component, and SLT uses zero moment, which is a better signal amplifier than DWT and discrete cosine transform (DCT). PSO is a popular optimization technique and is used to balance failure with success, because PSO increases the weight in the embedding process. As we know that SLT and PSO have been used by researchers for watermarks, this paper is used to examine the potential of SLT–PSO to be understood without compromising its power. In this paper, a technique designed to improve SLT performance during experiments on medical images is demonstrated. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Discrete wavelet transforms

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Modified MobileNetV2 with Convolutional Block Attention Module for Facial Expression Recognition

Modified MobileNetV2 with Convolutional Block Attention Modu...

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Artificial Intelligence and Image Processing (IWAIIP), International Workshop on

作者： Muh. Hafidh Ilmi Nafi’An Fitra Abdurrachman Bachtiar Budi Darma Setiawan Informatics Engineering Department Computer Science Faculty Brawijaya University Malang Indonesia

Facial emotion recognition is one of the artificial intelligence implementations used to recognize emotions based on data learned by computers. Unlike humans, who can recognize a person’s emotions directly, computers need several trainings procedures conducted by humans to be able to recognize a person’s emotions. Previous research has proposed various methods with deep learning and traditional machine learning approaches to classify emotions based on faces. Some studies obtained relatively high accuracy, but on evaluation by cross-validation, the results were much lower than the accuracy obtained. Therefore, this study proposes an approach using a modified MobileNetV2 deep learning architecture in the residual layer by adding a Convolutional Block Attention Module (CBAM) to improve accuracy and data generalization. This experiment uses the Karolinska Directional Emotional Faces (KDEF) public benchmark dataset. Preprocessing is done with image augmentation to enrich the training data by resizing, center cropping, random horizontal flipping, random affine, and random rotation. The proposed method is evaluated by 5-fold cross-validation and compared with MobileNetV2 without modification and state-of-the-art methods. Experimental results show that the proposed model outperform all comparative methods by achieving a 5 -fold cross-validation accuracy of 94.387%.

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