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Lecture Notes on Data engineering and Communications Technologies 2023年 180卷 178-186页

作者： Rahman, Md. Hafizur Noman, S.M. Salehin, Imrus Akhund, Tajim Md. Niamat Ullah Department of Digital Anti-Aging Healthcare Inje University Gimhae Korea Republic of Faculty of Computer Science and Engineering Frankfurt University of Applied Sciences Frankfurt Germany Department of Computer Engineering Dongseo University Busan Korea Republic of Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh

In past decades, scientific research has explored various bat detection systems methods, a common trend to identify heterogeneous species of bats. In this article, we represented an ultrasonic bat detection and protection method by interfacing an IoT automated device for the first time in agriculture development to introduce steady security in a preventive way. The automated bat banisher system is an effectual model for farmers to protect their land at a very affordable cost from nuisance bats. Moreover, the highest-quality fruits are the ones that bats choose to eat when they attack. This prevents the farmer from getting the best possible yield, Farmers have tried everything from handcrafted sound systems and medication to nets and traps to get rid of the bats, but they have yet to be effective. We created an Internet of Things-based automated bat banisher to combat bats invading farms. In this research, our High-Frequency Ultrasound (IoT automated) System has the potential to save farmers money, time, and effort. Farmers may increase agricultural output to provide the highest suitable fruit supply. Therefore, an ultrasonic IoT-based bat Banisher device is the most straightforward and functional means of dealing with nuisance bats. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Fruits

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A DL-DeNoiseNet Framework for Robust Image Denoising and Artifact Reduction in Medical Image Diagnostics 2

A DL-DeNoiseNet Framework for Robust Image Denoising and Art...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Nanthini, S. Maindola, Meenakshi Madhu, R. Ravishankar, M.D. Gandhi, Jay Patil, Harshal Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Mechanical Engineering The National Institute of Engineering Mysore India Faculty of Engineering and Technology Parul institute of Engineering and Technology Vadodara India School of Computer Science and Engineering IILM University Greater Noida India

ISBN: (纸本)9798331504960

For precise illness diagnosis and therapy planning, medical imaging diagnostics are necessary. Nevertheless, diagnostic errors can occur due to noise and aberrations that are inherent to imaging modalities including CT, MRI, and ultrasound, which can hide important features. In order to overcome this obstacle, we present a medical diagnostics-specific framework for deep learning-based picture denoising and artifact reduction. Our method improves feature extraction while maintaining picture integrity by combining a Convolutional Neural Network (CNN) with a Residual Dense Block (RDB). To guarantee optimal performance across varying degrees of noise and image types, the framework is trained on a broad dataset of medical photos, which includes images that are artifact-laden and noisy. A combination of supervised and adversarial learning techniques is used for this purpose. Adaptive filtering for preprocessing, the CNN-RDB network for denoising, and a refinement module that uses perceptual loss to keep things clinically relevant for postprocessing are the three primary steps in the methodology. The model outperforms traditional denoising methods when tested on various medical imaging metrics, including a PSNR of 40.3 dB and an SSIM of 0.97. Furthermore, the suggested architecture successfully mitigates artifacts by an average of 35% as assessed by the Normalized Root Mean Squared Error (NRMSE), guaranteeing that essential diagnostic characteristics are preserved. The results show that the model can improve image clarity, which means that diagnoses can be made more accurately and fewer scans are needed. Better healthcare outcomes are a direct result of the substantial enhancements to picture quality that this framework provides to physicians and radiologists. © 2024 IEEE.

关键词： Adaptive filtering

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VSSB-Raft: A Secure and Efficient Zero Trust Consensus Algorithm for Blockchain

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ACM Transactions on Sensor Networks 2024年第2期20卷 1-22页

作者： Tian, Siben Bai, Fenhua Shen, Tao Zhang, Chi Gong, Bei Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Key Laboratory of Computer Technology Applications China Faculty of Information Technology Beijing University of Technology China

To solve the problems of vote forgery and malicious election of candidate nodes in the Raft consensus algorithm, we combine zero trust with the Raft consensus algorithm and propose a secure and efficient consensus algorithm -Verifiable Secret Sharing Byzantine Fault Tolerance Raft Consensus Algorithm (VSSB-Raft). The VSSB-Raft consensus algorithm realizes zero trust through the supervisor node and secret sharing algorithm without the invisible trust between nodes required by the algorithm. Meanwhile, the VSSB-Raft consensus algorithm uses the SM2 signature algorithm to realize the characteristics of zero trust requiring authentication before data use. In addition, by introducing the NDN network, we redesign the communication between nodes and guarantee the communication quality among nodes. The VSSB-Raft consensus algorithm proposed in this paper can make the algorithm Byzantine fault tolerant by setting a threshold for secret sharing while maintaining the algorithm's complexity to be O(n). Experiments show that the VSSB-Raft consensus algorithm is secure and efficient with high throughput and low consensus latency. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Blockchain

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A Multiresolution Chain Coding Scheme for Different Resolution Ratios 9

A Multiresolution Chain Coding Scheme for Different Resoluti...

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9th International Conference on Mathematics and computers in sciences and Industry, MCSI 2024

作者： Zalik, Krista Rizman University of Maribor Faculty of Electrical Engineering and Computer Science Faculty of Natural Sciences and Mathematics Maribor Slovenia

ISBN: (纸本)9798350377224

A multiresolution chain coding scheme for contours based on Freeman chain codes in four direction is proposed. It progressively refines the grid size and encodes by the proposed R11 chain codes. Encoding and decoding processes are performed using a new hash function. The algorithm generates a multiresolution representation of a contour for the resolution ratio n:1, for any positive whole number n. The new R11 code is at least 12,5% better than the concatenation of F4 chain codes for various resolutions. The multiresolution generation of a multi-scale representation allows the algorithm to perform well within time, storage, or transmission constraints. © 2024 IEEE.

关键词： Contour followers

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Improving Accuracy Performance on CNN and BiLSTM Algorithms for Classification of Public Complaints Against Police Through WhatsAPP 8

Improving Accuracy Performance on CNN and BiLSTM Algorithms ...

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8th International Conference on Information Technology, Information Systems and Electrical engineering, ICITISEE 2024

作者： Oktarino, Ade Defit, Sarjon Yuhandri Adiwangsa Jambi University Faculty of Engineering and Computer Science Jambi City Indonesia Universitas Putra Indonesia 'YPTK' Faculty of Computer Science Padang Indonesia

ISBN: (纸本)9798350368970

The Indonesian republic police have provided services to the community by facilitating reporting via WhatsApp. However, messages sent through WhatsApp require manual identification to determine the type of offense reported by the public. Therefore, this research aims to assist in this process by applying deep learning approaches, specifically Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM). Despite the high accuracy of CNN and BiLSTM in some classifications, their performance varies depending on the dataset. This study enhances both the architecture and data preprocessing, including class balancing using SMOTE. To prevent overfitting, early stopping is applied during training. The optimization process in this study utilizes the Adam optimizer, known for its efficiency in handling large-scale data and its ability to adapt the learning rate. The experimental results indicate that while early stopping effectively mitigates overfitting, the resulting accuracy of 68% is not optimal. Without early stopping, the accuracy improves to 91 % for CNN and 98% for BiLSTM. These findings confirm that stopping models at the right moment and the choice of data augmentation and split techniques significantly impact the overall accuracy, making them crucial aspects of this research. © 2024 IEEE.

关键词： Convolutional neural networks

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Mitigating Ransomware in Government-Managed Institutions: A Global Critical Information Infrastructure Perspective

Mitigating Ransomware in Government-Managed Institutions: A ...

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20th International Conference on Cyber Warfare and Security, ICCWS 2025

作者： Mashila, Musiwalo Sithungu, Siphesihle Philezwini Lebea, Khutso Academy of Computer Science and Software Engineering Faculty of Science University of Johannesburg South Africa

ISBN: (纸本)9781917204316

This paper examines the escalating ransomware threats faced by government-managed educational institutions, focusing on their vulnerabilities, case studies, and mitigation strategies. With the adoption of Bring Your Own Device (BYOD) policies, schools increasingly expose their networks to cyber risks, making them attractive targets for cybercriminals. Case studies, including attacks on the Los Angeles Unified School District and the University of California, San Francisco, illustrate the profound impact of ransomware incidents and the diverse responses of institutions. Effective cybersecurity measures are crucial, emphasizing the need for prioritized spending, comprehensive security training, and advanced detection and response strategies. The role of government is also vital, as it develops legislation, guidelines, and funding opportunities to enhance educational cybersecurity. Recommendations include technical measures to secure networks and collaborative educational initiatives to share best practices. Ultimately, this research study underscores the necessity of continuous adaptation and government support in fortifying defences against ransomware threats. By fostering a cybersecurity awareness and resilience culture, educational institutions can better protect sensitive data and ensure the safety of their operations in an increasingly tricky digital landscape. © 2025 Proceedings of the International Conference on Information Warfare and Security (ICCWS). All rights reserved.

关键词： Cyber attacks

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Text Augmentation-Based Model for Emotion Recognition Using Transformers

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computers, Materials & Continua 2023年第9期76卷 3523-3547页

作者： Fida Mohammad Mukhtaj Khan Safdar Nawaz Khan Marwat Naveed Jan Neelam Gohar Muhammad Bilal Amal Al-Rasheed Department of Computer Science The University of HaripurHaripur22620Pakistan Department of Computer Systems Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and Technology PeshawarPeshawar25120Pakistan Department of Electronics Engineering Technology University of TechnologyNowshera24100Pakistan Department of Computer Science Shaheed Benazir BhuttoWomen UniversityPeshawar25000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Emotion Recognition in Conversations(ERC)is fundamental in creating emotionally ***-BasedNetwork(GBN)models have gained popularity in detecting conversational contexts for ERC ***,their limited ability to collect and acquire contextual information hinders their *** propose a Text Augmentation-based computational model for recognizing emotions using transformers(TA-MERT)to address *** proposed model uses the Multimodal Emotion Lines Dataset(MELD),which ensures a balanced representation for recognizing human *** used text augmentation techniques to producemore training data,improving the proposed model’s *** encoders train the deep neural network(DNN)model,especially Bidirectional Encoder(BE)representations that capture both forward and backward contextual *** integration improves the accuracy and robustness of the proposed ***,we present a method for balancing the training dataset by creating enhanced samples from the original *** balancing the dataset across all emotion categories,we can lessen the adverse effects of data imbalance on the accuracy of the proposed *** results on the MELD dataset show that TA-MERT outperforms earlier methods,achieving a weighted F1 score of 62.60%and an accuracy of 64.36%.Overall,the proposed TA-MERT model solves the GBN models’weaknesses in obtaining contextual data for ***-MERT model recognizes human emotions more accurately by employing text augmentation and transformer-based *** balanced dataset and the additional training samples also enhance its *** findings highlight the significance of transformer-based approaches for special emotion recognition in conversations.

关键词： Emotion recognition in conversation graph-based network text augmentation-basedmodel multimodal emotion lines dataset bidirectional encoder representation for transformer

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Building Detection from Satellite Images Using Deep Learning

Building Detection from Satellite Images Using Deep Learning

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2024 Multimedia University engineering Conference, MECON 2024

作者： Yap, Yong Loong Lim, Sin Liang Jatmiko, Wisnu Azizah, Kurniawati Hilman, Muhammand Hafizhuddin Multimedia University Faculty of Engineering Selangor Cyberjaya Malaysia Universitas Indonesia Faculty of Computer Science Indonesia

ISBN: (纸本)9798331530747

Satellite photography has transformed our capacity to comprehend and address dynamic alterations in our surroundings. Automated identification of buildings in satellite imagery is essential for urban planning and disaster management purposes. Conventional techniques frequently face challenges when dealing with the intricate and diverse nature of satellite imagery, requiring the use of more sophisticated methods. The research aims to improve building detection accuracy by utilizing deep learning methods, primarily Convolutional Neural Networks (CNNs, and more specifically the YOLOv8 architecture). The suggested methodology includes creating and using a CNN model, YOLOv8, designed to properly detect buildings and evaluate building damage levels in satellite pictures. The YOLOv8 architecture is intricately crafted to capture complex spatial hierarchies found in satellite data. Training and validation processes are carried out utilizing annotated datasets, with iterative adjustments made to enhance model performance. Post-disaster analysis is a crucial stage where a CNN model created using YOLOv8 is used to assess building damage and comprehend the effects of natural catastrophes on urban infrastructure. Heatmaps are created as visual aids to show the level of harm, helping stakeholders make decisions. Continual enhancements to the model are guided by analysis conducted after a disaster, ensuring continuous development and flexibility. The project intends to enhance building detection approaches using deep learning, particularly YOLOv8 based Convolutional Neural Networks, for disaster response and urban planning. Combining CNNs with satellite images provides a potent tool for spatial analysis and decision-making, impacting emergency management and sustainable urban development. © 2024 IEEE.

关键词： Satellite imagery

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Information Centric Networking Based Cooperative Caching Framework for 5G Communication Systems

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computers, Materials & Continua 2024年第9期80卷 3945-3966页

作者： R.Mahaveerakannan Thanarajan Tamilvizhi Sonia Jenifer Rayen Osamah Ibrahim Khalaf Habib Hamam Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India Department of Computer Science and Engineering Panimalar Engineering CollegeChennai600123India Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyChennai600119India Department of Solar Energy Al-Nahrain Research Center for Renewable EnergyAl-Nahrain UniversityJadriyaBaghdad64074Iraq Faculty of Engineering Universitéde MonctonNew BrunswickE1A3E9Canada Hodmas University College Mogadishu521376Somalia Bridges for Academic Excellence Tunis1002Tunisia School of Electrical Engineering University of JohannesburgAuckland ParkJohannesburg2006South Africa

The demands on conventional communication networks are increasing rapidly because of the exponential expansion of connected multimedia *** light of the data-centric aspect of contemporary communication,the information-centric network(ICN)paradigm offers hope for a solution by emphasizing content retrieval by name instead of *** 5G networks are to meet the expected data demand surge from expanded connectivity and Internet of Things(IoT)devices,then effective caching solutions will be required tomaximize network throughput andminimize the use of ***,an ICN-based Cooperative Caching(ICN-CoC)technique has been used to select a cache by considering cache position,content attractiveness,and rate *** findings show that utilizing our suggested approach improves caching regarding the Cache Hit Ratio(CHR)of 84.3%,Average Hop Minimization Ratio(AHMR)of 89.5%,and Mean Access Latency(MAL)of 0.4 *** a framework,it suggests improved caching strategies to handle the difficulty of effectively controlling data consumption in 5G *** improvements aim to make the network run more smoothly by enhancing content delivery,decreasing latency,and relieving *** improving 5G communication systems’capacity tomanage the demands faced by modern data-centric applications,the research ultimately aids in advancement.

关键词： Information-centric networking caching schemes 5G communication non-negative matrix factorization(NMF) weighted clustering algorithm

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Dynamic UAV Deployment in Multi-UAV Wireless Networks: A Multi-Modal Feature-Based Deep Reinforcement Learning Approach

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IEEE Internet of Things Journal 2025年第12期12卷 18765-18778页

作者： Bai, Yu Xie, Boxuan Liu, Ying Chang, Zheng Jäntti, Riku Aalto University Department of Information and Communications Engineering Espoo02150 Finland University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China University of Jyväskylä Faculty of Information Technology Jyväskylä40014 Finland

The use of Unmanned Aerial Vehicles (UAVs) as aerial base stations has attracted increasing research interest in recent years. A key challenge in this field is determining how to deploy multiple UAVs in dynamic environments, particularly where mobile user demands fluctuate. To address this challenge, this paper presents an adaptive UAV deployment scheme in a dynamic multi-UAV wireless network, considering the mobility of UAVs and users, state variability, and adjustable UAV transmission power. By jointly optimizing the UAVs’ operational modes, transmission power levels, and movement strategies, our objective is to achieve a trade-off between minimizing power consumption and maximizing ground user coverage. A Deep Reinforcement Learning (DRL) approach is proposed to address these challenges. To capture the dynamic variations of users and UAVs in the environment, a multi-modal feature state space is designed, consisting of both a multi-channel image and vectors. The image component integrates real-time data on user distribution and the UAV coverage area, while the vectors represent UAV operational modes, position data, and system temporal information. These multi-modal features are processed using a combination of Convolutional Neural Networks (CNNs) and Multilayer Perceptrons (MLPs) for advanced feature extraction. To enhance training stability and efficiency, the proposed approach updates parameters using the Proximal Policy Optimization (PPO) method. Simulation results demonstrate the effectiveness of the proposed scheme in balancing power consumption and coverage while effectively managing system dynamics. © 2014 IEEE.

关键词： Deep reinforcement learning

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