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

作者： Dwi Shinta Angreni Yusuf Anshori Wirdayanti Suci Ramadhani Arifin Fitrah Maharani Humaira Departement of Information Technology Faculty of Engineering Universitas Tadulako Palu Indonesia Departement of Informatics Engineering Universitas Dipa Makassar Makassar Indonesia Departemen of Informatics and Computer Engineering Politeknik Elektronika Negeri Surabaya Surabaya Indonesia

The cocoa industry serves as a vital economic pillar, holding a pivotal role in the daily lives of farmers in Central Sulawesi. However, a prevailing challenge for cocoa cultivators in this region resides in the inherently subjective nature of cocoa bean quality assessment, often necessitating the involvement of a substantial human workforce. Addressing this challenge requires the implementation of computer vision technology capable of autonomously grading cocoa bean quality. Such a system not only expedites the evaluation process but also enhances the precision and consistency in determining the quality of the beans. The study employed Convolutional Neural Networks (CNN) methodology to analyze images of cocoa beans sourced from Central Sulawesi farmers. The research demonstrated a remarkable accuracy of 94.66%, accompanied by a minimal loss of 0.1195. The model demonstrates strong performance on both training and validation sets, indicating effective learning of relevant patterns. Consistent accuracy and loss trends across epochs signify stable training without notable overfitting or underfitting. The minimal gap between training and validation metrics reflects the model's effective generalization to unseen data. This technological integration promises to revolutionize cacao quality assessment, offering a sophisticated and efficient solution for the benefit of Central Sulawesi's cacao farming community.

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Advanced Bandwidth Management Techniques Using Wireless Mesh Networks for High-Speed Optical Fiber Communications

Advanced Bandwidth Management Techniques Using Wireless Mesh...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Rakesh Kumar Yadav Sunil Sharma Kanika Seth Jeyalaxmi M Yashwant S. Ingle Vivek V Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune INDIA Faculty of Engineering and Technology Computer Science and Engineering JAIN (Deemed-to-be University) Bangalore Karnataka India

This abstract encompasses superior bandwidth management techniques the usage of Wi-Fi mesh networks to improve the performance of high-velocity optical fiber communications. the point of interest lies at the access-, relay-and center-mesh architectures with their diverse, discrete techniques consisting of dynamic frequency allocation, load balancing and node optimization, as well as on highlights from latest research. Within the experiments, the effects exhibit giant enhancement in signal first-rate and effectiveness of bandwidth resources when the use of the proposed version, based totally at the convergence speed of the network fee. Furthermore, it's also evident that the optimized wireless mesh configuration brings about enormous fee financial savings. All the findings obtained at some stage in this research have eminently greater the talents of the proposed structure for growing green communications networks.

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Explainable AI for Android Malware Detection: Towards Understanding Why the Models Perform So Well?

arXiv

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arXiv 2022年

作者： Liu, Yue Tantithamthavorn, Chakkrit Li, Li Liu, Yepang Faculty of Information Technology Monash University Melbourne Australia Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

Machine learning (ML)-based Android malware detection has been one of the most popular research topics in the mobile security community. An increasing number of research studies have demonstrated that machine learning is an effective and promising approach for malware detection, and some works have even claimed that their proposed models could achieve 99% detection accuracy, leaving little room for further improvement. However, numerous prior studies have suggested that unrealistic experimental designs bring substantial biases, resulting in over-optimistic performance in malware detection. Unlike previous research that examined the detection performance of ML classifiers to locate the causes, this study employs Explainable AI (XAI) approaches to explore what ML-based models learned during the training process, inspecting and interpreting why ML-based malware classifiers perform so well under unrealistic experimental settings. We discover that temporal sample inconsistency in the training dataset brings over-optimistic classification performance (up to 99% F1 score and accuracy). Importantly, our results indicate that ML models classify malware based on temporal differences between malware and benign, rather than the actual malicious behaviors. Our evaluation also confirms the fact that unrealistic experimental designs lead to not only unrealistic detection performance but also poor reliability, posing a significant obstacle to real-world applications. These findings suggest that XAI approaches should be used to help practitioners/researchers better understand how do AI/ML models (i.e., malware detection) work-not just focusing on accuracy improvement. Copyright © 2022, The Authors. All rights reserved.

关键词： Android malware

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An Optimized Deep Learning Framework for Semantic Interpretation Of Complex Single-Line Diagram Imagery

SSRN

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SSRN 2025年

作者： Bhanbhro, Hina Yew, Kwang Hooi Kusakunniran, Worapan Bin Zakaria, Mohammad Nordin Hashmi, Syed Abdul Moiz Amur, Zaira Hassan Memon, Vengas Computer and Information Science Department Universiti Teknologi Petronas Seri Iskandar Perak Darul Ridzuan Malaysia Faculty of Information and Communication Technology Mahidol University Nakhon Pathom Thailand Chemical Engineering Department Universiti Teknologi Petronas Seri Iskandar Perak Darul Ridzuan Malaysia

Accurate interpretation of single-line diagrams (SLDs) is crucial for analyzing electrical systems, as they encapsulate vital information about operational safety and efficiency in a simplified format. Traditional SLD processing methods rely on manual inspection and basic image analysis, which are computationally intensive, error-prone, and require extensive preprocessing. Although deep learning has been applied to symbol classification, existing models often fail to capture fine-grained symbol details, leading to misclassification. To address these limitations, this study proposes a hybrid deep learning-based symbol classification method. A newly created dataset was benchmarked using state-of-the-art deep learning models, and an optimal model was systematically designed, developed, and tested. The proposed approach integrates a Hybrid Residual Attention Module (HRAM) to enhance the model’s ability to identify fine-grained symbol details and a Proximity-aware Loss Function to improve performance in cluttered regions by penalizing missed classifications based on the spatial proximity of neighboring symbols. These innovations result in an optimized method for semantic processing in symbol classification tasks. The proposed model achieves 92.5% classification accuracy, a 3.8% improvement over the top-performing baseline, alongside a 19.6% reduction in model parameters. These advancements contribute to more efficient and accurate semantic processing of SLDs, paving the way for improved analysis of electrical system diagrams. © 2025, The Authors. All rights reserved.

关键词： Deep learning

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A study of the leverage effect in Bitcoin Conditional Heteroskedasticity prediction

A study of the leverage effect in Bitcoin Conditional Hetero...

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Conference on Artificial Intelligence and Robotics and Asia-Pacific International Symposium

作者： Masoud Omidvari Abarghouie Sasan H. Alizadeh Ahmad Khademzadeh Department of Computer Engineering Science and Research branch Islamic Azad University Tehran Iran Faculty of Information Technology Iran Telecommunication Research Center Tehran Iran Iran Telecommunication Research Center Tehran Iran

In today's world, countries establish large companies specifically designed to effectively manage the risks associated with volatility exchange rates and leveraged assets. Bitcoin is widely recognized as the leading cryptocurrency, making it highly appealing to investors and traders. The recorded data reveals a growing number of transactions conducted using Bitcoin cryptocurrency. Consequently, accurately predicting the volatility of Bitcoin's conditional heteroskedasticity holds significant importance for the financial markets. Cryptocurrencies present a challenging situation due to their complex dynamics, extreme observations, asymmetry, and various other non-linear features. Modeling these cryptocurrencies accurately becomes difficult using statistical models, as they fail to consider the leverage effect and provide precise predictions. In this paper, we have employed a hybrid modeling approach by utilizing MLP neural network and GARCH statistical model. The experiments conducted on Bitcoin time series data have yielded promising results, showcasing a notable reduction in the prediction error of conditional heteroskedasticity volatility.

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AI-Powered Real-Time Obstacle Detection for UAVs using Blockchain

AI-Powered Real-Time Obstacle Detection for UAVs using Block...

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International Conference on Parallel Distributed and Grid Computing (PDGC)

作者： Aparna Kumari Patel Ansh Alkeshbhai Shah Akshat Nimesh Safaa Najah Saud Al-Humairi Prasun Kumar Sunil Kumar Prakriti Lohumi Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad India Department of Computer Science and Engineering Faculty of Engineering Science and Technology Adani University Ahmedabad India Faculty of Information Sciences and Engineering Management and Science University Malaysia Department of Biotechnology SSET Sharda University Greater Noida U.P. India Department of AI and Data Science Guru Jambheshwar University of Science & Technology Hisar Haryana India Department of Artificial Intelligence and Data Science Bhagwan Parshuram Institute of Technology New Delhi Delhi India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

AI integration with UAVs has brought significant advancements in aerial control and operational safety, particularly in real-time obstacle detection—an essential aspect for navigating unknown environments. This work introduces an innovative AI-based solution for obstacle detection in UAVs, leveraging deep learning techniques to enhance precision and environmental awareness. The system’s architecture involves multiple layers, where UAVs first capture high-resolution images that undergo a processing pipeline including data pre-processing, augmentation, and labeling. A key element of this process is the use of Convolutional Neural Networks (CNNs) to train models capable of identifying obstacles across various terrains. To ensure the integrity and security of the data, especially in complex multi-UAV systems, blockchain technology is integrated. Utilizing Distributed Hash Tables (DHTs) and the Interplanetary File System (IPFS), this decentralized system creates a content-addressable database to store and authenticate unalterable records. Experimental analysis demonstrates that this system offers high accuracy in real-time obstacle detection, minimizing false positives and improving UAV safety.

关键词： Deep learning Autonomous aerial vehicles Real-time systems Blockchains Safety Reliability InterPlanetary File System Convolutional neural networks Artificial intelligence Drones

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Blockchain-Based Crowdsourcing in UAV-Assisted Vehicular Edge Computing

Blockchain-Based Crowdsourcing in UAV-Assisted Vehicular Edg...

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IEEE Globecom Workshops

作者： Ziqing Yu Zheng Chang Jian Li Yuan Li Yulu Zhang School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Information Technology University of Jyväskylä Jyväskylä Finland School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Department of IoT Technology Research China Mobile Research Institute Beijing China

Utilizing the flexibility advantages of the unmanned aerial vehicle (UAV), the emerging vehicular edge computing (VEC) is able to expand service and computing capability to the vehicular network. In this paper, we consider a crowdsourcing problem and propose a joint blockchain-based framework that enables end users to request services from UAV-assisted VEC system in emergency scenarios. Our framework leverages the global data relevant to the task by uploading it to the public blockchain for storage and tracing, where UAV and vehicles play important roles. Meanwhile, data specific to private networks is stored in the consortium blockchain to relieve data pressure on the public blockchain and improve the efficiency of the lookup process of public blockchain. During the task processing phase, we create a two-layer contract for the crowdsourcing network to incentivize the UAV network and vehicles to participate in task processing phase. We validate the feasibility of our two-layer contract through extensive simulations, which demonstrate the potential of our proposed framework to improve the efficiency of crowdsourcing in an UAV-assisted VEC.

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A Sentiment Analysis Study of Twitter Users' Reactions to the COVID-19 Vaccine

A Sentiment Analysis Study of Twitter Users' Reactions to th...

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International Conference on information and Communication Systems (ICICS)

作者： Suboh Alkhushayni Zakaria Alomari Du'a Al-Zaleq Dept. Information Systems Faculty of Information Technology and Computer Science Yarmouk University Irbid Jordan Dept. Computer Science New York Institute of Technology Vancouver BC Canada Dept. Robotics and Artificial Intelligence Engineering Al-Ahliyya Amman University Amman Jordan

This study investigates public attitudes towards the COVID-19 vaccine through Twitter data analysis. Using the Twitter API, tweets were collected, preprocessed, and labeled. Features were extracted using the Bag of Words representation, and sentiment analysis was conducted using Text Blob and Vader. Machine learning and deep learning models were trained and tested, revealing that deep learning models achieved the highest accuracy and F1 score. The research underscores the efficacy of machine learning and deep learning in analyzing COVID-19 vaccine-related tweets, shedding light on factors influencing vaccine resistance. These insights are crucial for pharmaceutical companies and public health officials, enabling them to address barriers to vaccine acceptance and enhance the societal benefits of widespread vaccination, contributing to the pandemic's resolution.

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CV-MOS: A Cross-View Model for Motion Segmentation

arXiv

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arXiv 2024年

作者： Tang, Xiaoyu Chen, Zeyu Cheng, Jintao Chen, Xieyuanli Wu, Jin Xue, Bohuan The School of Electronic and Information Engineering Faculty of Engineering South China Normal University Guangdong Foshan528225 China Xingzhi College South China Normal University Guangdong Guangzhou510000 China The College of Intelligence Science and Technology National University of Defense Technology Changsha China The Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong The Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

In autonomous driving, accurately distinguishing between static and moving objects is crucial for the autonomous driving system. When performing the motion object segmentation (MOS) task, effectively leveraging motion information from objects becomes a primary challenge in improving the recognition of moving objects. Previous methods either utilized range view (RV) or bird’s eye view (BEV) residual maps to capture motion information. Unlike traditional approaches, we propose combining RV and BEV residual maps to exploit a greater potential of motion information jointly. Thus, we introduce CV-MOS, a cross-view model for moving object segmentation. Novelty, we decouple spatial-temporal information by capturing the motion from BEV and RV residual maps and generating semantic features from range images, which are used as moving object guidance for the motion branch. Our direct and unique solution maximizes the use of range images and RV and BEV residual maps, significantly enhancing the performance of LiDAR-based MOS task. Our method achieved leading IoU(%) scores of 77.5% and 79.2% on the validation and test sets of the SemanticKitti dataset. In particular, CV-MOS demonstrates SOTA performance to date on various datasets. The CV-MOS implementation is available at https://***/SCNU-RISLAB/CV-MOS Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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DNCP: A Robust Artificial Intelligence Based Alzheimer's Disease Detection Methodology by Using Deep Neural Classification Principle

DNCP: A Robust Artificial Intelligence Based Alzheimer's Dis...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： R.Jeya Malar K. Devanandam Nanammal V Ch. Hemanth Kumar Bonde A Suha Afaneh Dept of Computer Science and Engineering Kings Engineering College Chennai Department of S&H QIS College of Engineering and Technology PrakasamDistrict AndhraPradesh Dept of ECE Jeppiaar Engineering College Chennai Department of MCA QIS College of Engineering and Technology AndhraPradesh Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur Department of Cybersecurity faculty of information technology Zarqa university Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Alzheimer's disease (AD) is a neurodegenerative disorder that causes cognitive decline, memory loss, and ultimately death. Early and accurate detection of AD is crucial for timely intervention and improving patient outcomes. This study proposes DNCP: a robust deep learning (DL) based classification methodology for AD detection. The model integrates advanced preprocessing techniques, such as image registration, intensity normalization, and noise reduction, to enhance the quality of neuroimaging data. A multimodal learning approach is utilized to combine features from specific brain regions of interest (ROI), including the hippocampus, gray matter, and white matter. Comparative analysis was conducted against state-of-the-art models such as CNNs (ResNet, VGG-16, AlexNet), RNNs, and AutoEncoders, demonstrating the superior performance of DNCP. The proposed model achieved an accuracy of 95.7 % , sensitivity of 94.1%, and specificity of 96.2%, outperforming all other models. The results show that the DNCP model provides an efficient and reliable solution for early-stage Alzheimer's detection, opening doors for enhanced diagnostic tools in clinical settings.

关键词： Neuroimaging Deep learning Accuracy Sensitivity Computational modeling Brain modeling Convolutional neural networks Reliability Alzheimer's disease White matter

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