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1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Al-Hadi, Marwa Abdulrahman Al-Gaphari, Ghaleb Hamoud Al-Baltah, Ibrahim Ahmed Julian, Francisco Bacallao Al-Hadi, Ahmed Abdulrahman Department of Computer Science Faculty of Computer Science and IT Sana’a University Sana’a Yemen Department of Information Technology Faculty of Computer Science and IT Sana’a University Sana’a Yemen Faculty of Science and Engineering Al-Hikma University Sana’a Yemen Cardiac-Center Military Hospital Yemen Department of Science and Technologies Faculty of Engineering and Technology Mohamed Boudiaf University M'sila Algeria Department of Industrial Electronic and Automatic Engineering Faculty of Higher Polytechnic Technology Burgos University Burgos Spain

ISBN: (纸本)9798331533557

In an era when healthcare was becoming increasingly crucial, many developing nations, including Yemen, struggled to provide basic medical services. Nearly half of Yemen's population lacked access to adequate healthcare, with the situation being even more dire in rural and remote areas. While sectors such as industry, agriculture, and environmental science rapidly embraced technological advancements, healthcare systems in these regions lagged, with limited research addressing affordable and accessible solutions. This gap was further highlighted by the global healthcare crisis and the urgent need for innovative, cost-effective approaches. In response, this study outlined the creation and deployment of a cost-effective health monitoring system utilizing Internet of Things (IoT) technology to address these challenges. The system was designed to monitor vital signs, specifically heart rate (HR), using an Arduino Uno and ECG sensor, at a meager total cost of $27.25. It provided real-time monitoring for situations requiring immediate intervention. Power consumption tests demonstrated that the system operated efficiently, consuming between 1.061 and 1.35 watts, making it practical and affordable. Additionally, the potential integration of deep learning techniques promises to enhance the system's accuracy and efficiency. Although this study focused on IoT-based health monitoring, its potential extended beyond this, offering broader implications for future healthcare technology solutions. © 2024 IEEE.

关键词： Electronic health record

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Advanced Feature Extraction and Machine Learning Techniques for Classifying Steam Game Feedback

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第1期19卷 107-125页

作者： Al Tawil, Arar Fathi, Hanaa Alzoubi, Sharaf Shaban, Amneh Almazaydeh, Laiali H. Faculty of Information Technology Applied Science Private University Amman Jordan College of Computer Science and Informatics Amman Arab University Amman Jordan College of Engineering AbuDhabi University Abu Dhabi United Arab Emirates

The gaming industry produces vast amounts of user-generated feedback, making it challenging for developers to efficiently analyze and respond to real-time reviews. This study addresses the problem of classifying large-scale unstructured user feedback from Steam reviews. In this paper an approach that integrates traditional machine learning models and deep learning models is proposed. XGBoost is used to manage feature-rich datasets, reducing overfitting. Long-short-term memory (LSTM) and Bi-directional LSTM are used to enhance the accuracy and robustness of classification. Feature extraction techniques, such as sentiment analysis and topic modeling, are employed to enrich the dataset and improve model performance. The experimental results show that the XGBoost model achieved the highest performance with an accuracy of 0.9499 and a ROC-AUC score of 0.6113, demonstrating superior performance in distinguishing between positive and negative feedback. In comparison, deep learning models such as LSTM and Bi-directional LSTM showed lower ROC-AUC scores, indicating less effectiveness in handling the classification task. This approach offers game developers a reliable and scalable solution for classifying user sentiment, leading to better game improvements based on user reviews. © 2025 by the authors of this article.

关键词： Contrastive Learning

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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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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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Application of support vector machine algorithm for early differential diagnosis of prostate cancer

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Data science and Management 2023年第1期6卷 1-12页

作者： Boluwaji A.Akinnuwesi Kehinde A.Olayanju Benjamin S.Aribisala Stephen G.Fashoto Elliot Mbunge Moses Okpeku Patrick Owate Department of Computer Science Faculty of Science and EngineeringUniversity of EswatiniKwaluseniM201Swaziland Department of Computer Science Education Federal College of Education(Technology)AkokaLagos State100213Nigeria Department of Computer Science Faculty of ScienceLagos State UniversityOjoLagos State102101Nigeria Department of Genetics University of KwaZulu-NatalDurban4041South Africa

Prostate cancer(PCa)symptoms are commonly confused with benign prostate hyperplasia(BPH),particularly in the early stages due to similarities between symptoms,and in some instances,*** methods have been utilized to diagnose PCa;however,at the full-blown stage,clinical methods usually present high risks of complicated side ***,we proposed the use of support vector machine for early differential diagnosis of PCa(SVM-PCa-EDD).SVM was used to classify persons with and without *** used the PCa dataset from the Kaggle Healthcare repository to develop and validate SVM model for *** PCa dataset consisted of 250 features and one class of *** considered in this study were age,body mass index(BMI),race,family history,obesity,trouble urinating,urine stream force,blood in semen,bone pain,and erectile *** SVM-PCa-EDD was used for preprocessing the PCa dataset,specifically dealing with class imbalance,and for dimensionality *** eliminating class imbalance,the area under the receiver operating characteristic(ROC)curve(AUC)of the logistic regression(LR)model trained with the downsampled dataset was 58.4%,whereas that of the AUC-ROC of LR trained with the class imbalance dataset was 54.3%.The SVM-PCa-EDD achieved 90%accuracy,80%sensitivity,and 80%*** validation of SVM-PCa-EDD using random forest and LR showed that SVM-PCa-EDD performed better in early differential diagnosis of *** proposed model can assist medical experts in early diagnosis of PCa,particularly in resource-constrained healthcare settings and making further recommendations for PCa testing and treatment.

关键词： Confusable diseases Computational intelligence Early differential diagnosis Logistic regression Prostate cancer Support vector machine

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EEDL-based Detection and Classification of Apple Foliar Leaf Disease 15

EEDL-based Detection and Classification of Apple Foliar Leaf...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Kumar, Satish Kumar, Rakesh Gupta, Meenu Obaid, Ahmed J. Chandigarh University Department of Computer Science and Engineering Punjab India University of Kufa National University of Science and Technology Faculty of Computer Science and Mathematics Dhi Qar Najaf Iraq

ISBN: (纸本)9798350370249

Apple farmers constantly grapple with the challenge of increasing their yield and safeguarding apple trees from diseases. The prevalence of diseases and pests significantly hampers apple production, resulting in substantial financial losses for the industry annually Detecting apple leaf diseases swiftly and accurately is crucial for effectively handling and curbing these issues within orchards. Specifically, advancements in computer vision methods that utilize deep learning have opened avenues for identifying and understanding these diseases at an early stage directly on the leaves Here, the EEDL(Enhanced Efficient Deep Learning)model is proposed for the detection and classification of five types of apple foliar diseases. In the EEDL model, we modify and fine-tune the Efficient Net model variant B0 by adding four additional layers. These layers consist of the augmentation layer, dense layer, dropout layer and the final output classifier dense layer. The AFD-7C dataset(8000 images are gathered in the real filed environment of apple research Centre and apple orchards of Himachal Pradesh. 9037 images are obtained from the Plant Village public dataset) is utilized in the experimental procedure and achieves an accuracy of 97.82%. © 2024 IEEE.

关键词： Fruits

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Solving the Minimal Positional Substring Cover Problem in Sublinear Space 35

Solving the Minimal Positional Substring Cover Problem in Su...

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35th Annual Symposium on Combinatorial Pattern Matching, CPM 2024

作者： Bonizzoni, Paola Boucher, Christina Cozzi, Davide Gagie, Travis Pirola, Yuri Department of Computer Science University of Milano-Bicocca Italy Department of Computer and Information Science and Engineering University of Florida GainesvilleFL United States Faculty of Computer Science Dalhousie University Halifax Canada

ISBN: (纸本)9783959773263

Within the field of haplotype analysis, the Positional Burrows-Wheeler Transform (PBWT) stands out as a key innovation, addressing numerous challenges in genomics. For example, Sanaullah et al. introduced a PBWT-based method that addresses the haplotype threading problem, which involves representing a query haplotype through a minimal set of substrings. To solve this problem using the PBWT data structure, they formulate the Minimal Positional Substring Cover (MPSC) problem, and then, subsequently present a solution for it. Additionally, they present and solve several variants of this problem: k-MPSC, leftmost MPSC, rightmost MPSC, and length-maximal MPSC. Yet, a full PBWT is required for each of their solutions, which yields a significant memory usage requirement. Here, we take advantage of the latest results on run-length encoding the PBWT, to solve the MPSC in a sublinear amount of space. Our methods involve demonstrating that k-Set Maximal Exact Matches (k-SMEMs) can be computed in a sublinear amount of space via efficient computation of k-Matching Statistics (k-MS). This leads to a solution that requires sublinear space for, not only the MPSC problem, but for all its variations proposed by Sanaullah et al. Most importantly, we present experimental results on haplotype panels from the 1000 Genomes Project data that show the utility of these theoretical results. We conclusively demonstrate that our approach markedly decreases the memory required to solve the MPSC problem, achieving a reduction of at least two orders of magnitude compared to the method proposed by Sanaullah et al. This efficiency allows us to solve the problem on large versions of the problem, where other methods are unable to scale to. In summary, the creation of µ-PBWT paves the way for new possibilities in conducting in-depth genetic research and analysis on a large scale. All source code is publicly available at https://***/dlcgold/muPBWT/tree/k-smem. © Paola Bonizzoni, Christina Boucher

关键词： Genome

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Wheat Pest Identification Based on Deep Learning Techniques 7

Wheat Pest Identification Based on Deep Learning Techniques

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7th IEEE International Conference on Big Data and Artificial Intelligence, BDAI 2024

作者： Li, Cheng Chen, Siyi Ma, Yueqi Song, Mengjie Tian, Xiaolin Cui, Hanwen Macau University of Science and Technology School of Computer Science and Engineering Faculty of Innovation Engineering China China Agricultural University School of Plant Protection Beijing China Zhuhai College of Science and Technology School of Computer Science Zhuhai China

ISBN: (纸本)9798350352009

In recent years, effective pest management has become of cross-application of artificial intelligence and big data technologies in agriculture and environment. Nowadays, plant pests have just become a serious problem, which affects the ecological health as well as the growth of crops. In order to further better target traditional crop wheat for pest management, this study utilizes advanced deep learning techniques to accurately detect and categorize common wheat pests ('wireworm', 'Beet spot flies', and 'Cicadellidae'). Through the comparative analysis with the baseline model ResNet-18 model, this study also introduces the more advanced models SE-RegNetY and SEInception-ResNet-V3 models for pest identification in previous studies. Notably, among the three models applied in this study, the SE-Inception-ResNet-V3 model showed excellent results, achieving a significant average overall accuracy of 81.50% and kappa coefficient of 72.24%, which were 10.3% and nearly 14.8% higher than the results of the benchmark ResNet-18 model, respectively. This study marks a further application of deep learning and artificial intelligence in agricultural pest management. By improving the accuracy of agricultural pest identification and classification, it provides the basis and foundation for more sustainable and environmentally friendly pest control methods. © 2024 IEEE.

关键词： Agriculture Deep Learning Techniques Image Processing Pest Identification Wheat

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A Novel Approach for Detection of Agricultural Crops Using CNN 14

A Novel Approach for Detection of Agricultural Crops Using C...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Mahesh, T.R. Singh Chandel, Aman Gupta, Harshitha Jain, Rakshit Garg, Sadhvi Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350335095

Crop classification is an important aspect of farming because it improves crop management and increases crop yield. This study proposes a CNN-based crop classification technique using high-resolution images. To use this method, we created a database of 30 crops, including jute, maize, almonds, rice, sugarcane, banana, wheat, and others. The Convolutional Neural Network (CNN) is an Artificial Intelligence-based algorithm that learns similar features from images and is capable of performing a variety of tasks such as image classification, object detection, and segmentation. In this system, CNN or ConvNet is used to learn the features provided in the input images by sequentially entering several layers such as Convolution, Activation function (RELU), Pooling, and Flattening. Finally, with only a few explanations in the training set, this providing outstanding classifies new images. The proposed method has two major components: preprocessing and categorization. The experimental results show that the proposed tactic worked extremely well, with a ground truth of 0.9264. © 2023 IEEE.

关键词： Convolution

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