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1st IEEE International Symposium on Consumer Technology, ISCT 2024

作者： Sugiartawan, Putu Funabiki, Nobuo Haz, Amma Lievarastranta Wardani, Ni Wayan Okayama University Departement of Information and Communication Systems Japan Faculty of Informatics Engineering Instiki Indonesia Okayama University Departement of Information and Communication Systems Okayama Japan

ISBN: (纸本)9798350365191

This research investigates the classification of dried moringa leaves using machine-learning techniques, focusing on texture and color features. The study addresses the challenge of overlapping classes A and B by merging them into a new class, AB. Several models were evaluated, including Support Vector Machine (SVM), Decision Tree, Gradient Boosting Decision Trees (GBDT), XGBoost, and LightGBM. The SVM model achieved the highest overall accuracy of 98 percent, significantly improving classification performance by resolving the class overlap issue. The confusion matrix demonstrated the SVM model's effectiveness, with minimal misclassifications and high accuracy across all classes. Ensemble methods, including GBDT, XGBoost, and LightGBM, also performed well, each achieving an overall accuracy of 98 percent, highlighting their robustness. However, the Decision Tree model lagged slightly with a 92 percent accuracy. The results suggest that SVM and ensemble methods are highly effective for complex classification tasks involving intricate feature sets. This study demonstrates the potential of combining machine learning techniques and feature engineering to enhance classification accuracy, providing valuable insights for agricultural and food processing applications. © 2024 IEEE.

关键词： Self-supervised learning

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A learning artificial visual system for motion direction detection

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Neural Computing and Applications 2024年第27期36卷 17181-17197页

作者： Chen, Tianqi Kobayashi, Yuki Todo, Yuki Tang, Zheng Faculty of Electrical and Computer Engineering Kanazawa University Kakuma Ishikawa Kanazawa9201162 Japan Department of Intelligence Information Systems University of Toyama Toyama9308555 Japan

Research on the visual system and its development is not only crucial for understanding how we see and interpret the environment from basic visual processing to complex perception but also aids in the development of technologies like virtual reality, augmented reality, and computer vision. In our previous paper, we have proposed a novel biological motion direction detection mechanism. Based on this mechanism, the authors of this paper proposed a learning artificial visual system (AVS) and use it to achieve motion direction detection. The learning AVS consists of two layers: the local visual feature detective neuron layer and the global feature detective neuron layer. For local motion direction detection, we use dendritic neurons to implement local motion direction-detective neurons and use them to extract local motion direction information. Global feature detective neurons are simply implemented by summing the local motion direction-detective neurons in each direction of possible motion. We trained the learning AVS by eliminating unnecessary branches or synapses and keeping only those that are strongly connected to the function of motion direction detection. Computer simulations demonstrate that AVS can learn the correct motion direction of objects with different shapes, size and positions. We compare the performance of AVS with support vector machine (SVM) and traditional convolutional neural networks (CNNs)—LeNet-5 and EfficientNet-B0 (EfN)—in motion direction detection and find that our learning AVS has better recognition accuracy, noise immunity and less demands on size of dataset and learning costs. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Neurons

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A Reputation-Based AODV Protocol for Blackhole and Malfunction Nodes Detection and Avoidance

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Computers, Materials & Continua 2024年第8期80卷 1867-1888页

作者： Qussai M.Yaseen Monther Aldwairi Ahmad Manasrah Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjman346United Arab Emirates Department of Computer Information Systems Faculty of Computer and Information TechnologyJordan University of Science and TechnologyIrbid22110Jordan College of Technological Innovation Zayed UniversityAbu Dhabi144534United Arab Emirates Faculty of Information Technology and Computer Science Yarmouk UniversityIrbid21163Jordan Computer Information Systems Higher Colleges of TechnologySharjah341214United Arab Emirates

Enhancing the security of Wireless Sensor Networks(WSNs)improves the usability of their ***,finding solutions to various attacks,such as the blackhole attack,is crucial for the success of WSN *** paper proposes an enhanced version of the AODV(Ad Hoc On-Demand Distance Vector)protocol capable of detecting blackholes and malfunctioning benign nodes in WSNs,thereby avoiding them when delivering *** proposed version employs a network-based reputation system to select the best and most secure path to a *** achieve this goal,the proposed version utilizes the Watchdogs/Pathrater mechanisms in AODV to gather and broadcast reputations to all network nodes to build the network-based reputation *** minimize the network overhead of the proposed approach,the paper uses reputation aggregator nodes only for forwarding reputation ***,to reduce the overhead of updating reputation tables,the paper proposes three mechanisms,which are the prompt broadcast,the regular broadcast,and the light broadcast *** proposed enhanced version has been designed to perform effectively in dynamic environments such as mobile WSNs where nodes,including blackholes,move continuously,which is considered a challenge for other *** the proposed enhanced protocol,a node evaluates the security of different routes to a destination and can select the most secure routing *** paper provides an algorithm that explains the proposed protocol in detail and demonstrates a case study that shows the operations of calculating and updating reputation values when nodes move across different ***,the paper discusses the proposed approach’s overhead analysis to prove the proposed enhancement’s correctness and applicability.

关键词： AODV blackhole malfunction nodes pathrater reputation system wireless sensor networks(WSNs) watchdogs

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Challenge-Response Emotion Authentication Algorithm Using Modified Horizontal Deep Learning

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Intelligent Automation & Soft Computing 2023年第3期35卷 3659-3675页

作者： Mohamed Ezz Ayman Mohamed Mostafa Ayman Elshenawy College of Computer and Information Sciences Jouf UniversitySakaka72314Saudi Arabia Systems and Computers Engineering DEPT Faculty of EngineeringAl-Azhar UniversityCairo11651Egypt Software Engineering&Information Technology Faculty of Engineering&TechnologyEgyptian Chinese UniversityEgypt

Face authentication is an important biometric authentication method commonly used in security *** is vulnerable to different types of attacks that use authorized users’facial images and videos captured from social media to perform spooﬁng attacks and dynamic movements for penetrating secur-ity *** paper presents an innovative challenge-response emotions authentication model based on the horizontal ensemble *** proposed model provides high accurate face authentication process by challenging the authorized user using a random sequence of emotions to provide a speciﬁc response for every authentication trial with a different sequence of *** proposed model is applied to the KDEF dataset using 10-fold *** improvements are made to the proposed ***,the VGG16 model is applied to the seven common ***,the system usability is enhanced by analyzing and selecting only the four common and easy-to-use ***,the horizontal ensemble technique is applied to enhance the emotion recognition accuracy and minimize the error during authen-tication ***,the Horizontal Ensemble Best N-Losses(HEBNL)is applied using challenge-response emotion to improve the authentication efﬁ-ciency and minimize the computational *** successive improvements implemented on the proposed model led to an improvement in the accuracy from 92.1%to 99.27%.

关键词： Face authentication challenge-response authentication transfer learning and horizontal ensemble technique

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Advancing Text-to-Image Generation: A Comparative Study of StyleGAN-T and Stable Diffusion 3 under Neutrosophic Sets

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Neutrosophic Sets and systems 2025年 85卷 784-800页

作者： Sadek, Mohamed G. Hassan, A.Y. Diab, Tamer O. Abdelhafeez, Ahmed Benha Engineering Faculty Egypt Electrical Engineering Department Benha Engineering Faculty Benha University 13511 Egypt Computer Science Department Faculty of Information Systems and Computer Science October 6th University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan

Recent advances in generative models have revolutionized the technology employed for image synthesis quite significantly, and two paradigms—GANs and diffusion-based models—are leading the pack of innovation. This paper outlines an extensive comparison and analysis of some of the best models across both paradigms, namely StyleGAN-T, DF-GAN, AttnGAN, and BigGAN on the GAN side and Stable Diffusion 3 (SD3), DALL·E 3, Midjourney v6, and Imagen 2 on the diffusion side. We systematically inspect the architectural design, training protocols, text-conditioning processes, and domain adaptability of each model, highlighting how they address text-to-image generation challenges differently. Through qualitative and quantitative measurements—such as FID, CLIP Score, human preference surveys, and compositional accuracy, the work reveals performance tradeoffs concerning speed, control, creativity, semantic alignment, and photorealism. We use the Neutrosophic Set model to select the best model based on these evaluation matrices. We have different scores for each model based on evaluation matrices. So, the neutrosophic set is used to overcome the uncertainty information. We use the COPRAS method to rank the models and select the best one based on the evaluation matrix weights. © 2025, University of New Mexico. All rights reserved.

关键词： Photointerpretation

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Fuzzy clustering for electric field characterization and its application to thunderstorm interpretability

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Digital Communications and Networks 2025年第2期11卷 299-307页

作者： Xu Yang Hongyan Xing Xinyuan Ji Wei Xu Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing 210044China Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 2R3Canada Systems Research Institute Polish Academy of Sciences00-901 WarsawPoland Department of Computer Engineering Faculty of Engineering and Natural SciencesIstinye University34010 IstanbulTurkey

Changes in the Atmospheric Electric Field Signal(AEFS)are highly correlated with weather changes,especially with thunderstorm ***,little attention has been paid to the ambiguous weather information implicit in AEFS *** this paper,a Fuzzy C-Means(FCM)clustering method is used for the first time to develop an innovative approach to characterize the weather attributes carried by ***,a time series dataset is created in the time domain using AEFS *** AEFS-based weather is evaluated according to the time-series Membership Degree(MD)changes obtained by inputting this dataset into the ***,thunderstorm intensities are reflected by the change in distance from a thunderstorm cloud point charge to an AEF ***,a matching relationship is established between the normalized distance and the thunderstorm dominant MD in the space ***,the rationality and reliability of the proposed method are verified by combining radar charts and expert *** results confirm that this method accurately characterizes the weather attributes and changes in the AEFS,and a negative distance-MD correlation is obtained for the first *** detection of thunderstorm activity by AEF from the perspective of fuzzy set technology provides a meaningful guidance for interpretable thunderstorms.

关键词： Atmospheric electric field(AEF) Thunderstorm Fuzzy C-means(FCM) Attribute

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Robust steganographic approach using generative adversarial network and compressive autoencoder

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Multimedia Tools and Applications 2024年 1-38页

作者： Qasaimeh, Malik Qtaish, Alaa Abu Aljawarneh, Shadi Department of Computer Information Systems Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan Department of Software Engineering co-joint with the Department of Cyber Security Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan

Nowadays, social media applications and websites have become a crucial part of people’s lives;for sharing their moments, contacting their families and friends, or even for their jobs. However, the fact that these valuable data are transferred via the Internet and open channels, which are vulnerable to attacks and espionage, requires using defense methods to improve the security of data transmission. Recently, the progress of Deep Learning (DL) inspired researchers to use it with security methods such as steganography, the art of hiding secret data in unrelated content. DL and steganography combined enhanced the hiding properties, especially in the Coverless Image Steganography (CIS) methods. In this paper, we propose a Compressive Coverless Image Steganography (CCIS) model, which is a generated-based CIS, to improve the capacity and robustness of Steganography. This model uses the compressive autoencoder to compress the message, thus increasing the capacity, and uses the Generative Adversarial Network (GAN) to generate a stego image from the compressed vector, thus enhancing concealment ability, besides using a regression model and optimization method to extract the data. Another version was designed to enable sending binary messages by replacing the compressive autoencoder network with a robust mapping rule. Experiments show that the proposed model could improve the characteristics of Steganography. Furthermore, the proposed model could extract binary messages with 256 bits from attacked stego images with 99% recovery accuracy. Thus, capacity and robustness were enhanced using our model with both images and text as secret messages. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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Attention Management: Extending in Progress 24

Attention Management: Extending in Progress

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24th IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2024

作者： Brodniansky, Juraj Lang, Ján Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak Technical University in Bratislava Bratislava Slovakia

ISBN: (纸本)9798350353433

Attention management in software development proves to be a highly relevant issue. It offers various ways of managing the user’s attention using visual perception of the work environment. Awareness of the workplace provides a user with work context which may positively affect the productivity and workflow of a project. However, the work context may also provide the user with arbitrary information. Thus, regulations of incoming workplace information are required throughout the user’s work cycle. Furthermore, previous research supports the proposed extensions devised according to the lacking parts in the existing project management system. Furthermore, to stabilize the extensions, results from an experiment conducted on the proof-of-concept designed according to the proposed parts. © 2024 IEEE.

关键词： Project management

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ERBM:A Machine Learning-Driven Rule-Based Model for Intrusion Detection in IoT Environments

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Computers, Materials & Continua 2025年第6期83卷 5155-5179页

作者： Arshad Mehmmod Komal Batool Ahthsham Sajid Muhammad Mansoor Alam Mazliham MohD Su’ud Inam Ullah Khan Department of Information Security and Data Science Riphah Institute of Systems EngineeringRiphah International UniversityIslamabad46000Pakistan Faculty of Computing Riphah International UniversityIslamabad46000Pakistan Faculty of Computing and Informatics Multimedia UniversityCyberjaya63100Malaysia

Traditional rule-based IntrusionDetection systems(IDS)are commonly employed owing to their simple design and ability to detect known ***,as dynamic network traffic and a new degree of threats exist in IoT environments,these systems do not perform well and have elevated false positive rates—consequently decreasing detection *** this study,we try to overcome these restrictions by employing fuzzy logic and machine learning to develop an Enhanced Rule-Based Model(ERBM)to classify the packets better and identify *** ERBM developed for this approach improves data preprocessing and feature selections by utilizing fuzzy logic,where three membership functions are created to classify all the network traffic features as low,medium,or high to remain situationally aware of the *** fuzzy logic sets produce adaptive detection rules by reducing data ***,for further classification,machine learning classifiers such as Decision Tree(DT),Random Forest(RF),and Neural Networks(NN)learn complex ways of attacks and make the detection process more precise.A thorough performance evaluation using different metrics,including accuracy,precision,recall,F1 Score,detection rate,and false-positive rate,verifies the supremacy of ERBM over classical *** extensive experiments,the ERBM enables a remarkable detection rate of 99%with considerably fewer false positives than the conventional *** the ability for uncertain reasoning with fuzzy logic and an adaptable component via machine learning solutions,the ERBM systemprovides a unique,scalable,data-driven approach to IoT intrusion *** research presents a major enhancement initiative in the context of rule-based IDS,introducing improvements in accuracy to evolving IoT threats.

关键词： Rule based intrusions IoT fuzzy prediction

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YOLOv8n-CGW: A novel approach to multi-oriented vehicle detection in intelligent transportation systems

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Multimedia Tools and Applications 2025年第7期84卷 3809-3840页

作者： Berwo, Michael Abebe Fang, Yong Sarwar, Nadeem Mahmood, Jabar Aljohani, Mansourah Elhosseini, Mostafa School of Information and Engineering Chang’an University Shaanxi Xi’an710064 China Department of Computer Science Bahria University Lahore Campus Punjab Lahore54600 Pakistan Department of Information Technology Faculty of Computing and Information Technology University of Sialkot Punjab Sialkot51040 Pakistan College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt

In the context of Intelligent Transportation systems (ITS), the role of vehicle detection and classification is indispensable for streamlining transportation management, refining traffic control, and conducting in-depth accident analyses. However, the intricate task of accurately detecting multi-oriented vehicles in diverse scenarios remains a challenge, even with the advancements in ITS. Factors such as vehicle morphology, design, road conditions, structural details, and climatic variables provide a significant part in the detection. To address this, our study introduces an advanced YOLOv8n model, specifically the YOLOv8n-CGW, which is built upon pre-trained deep learning frameworks. This model is meticulously tailored to enhance the detection capabilities for multi-oriented vehicles. Comprehensive evaluations on esteemed datasets like MtV, MERGED, Custom dataset, DAWN, WEDGE, and UA-DETRAC underscore the superiority of our approach over existing models. Notably, our YOLOv8n-CGW model achieves a remarkable mAP of 83.3% and boasts a swift inference time of 0.0019s, setting a new benchmark in the domain. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

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